TY - JOUR A1 - Hirling, Dominik A1 - Tasnadi, Ervin A1 - Caicedo, Juan A1 - Caroprese, Maria V. A1 - Sjögren, Rickard A1 - Aubreville, Marc A1 - Koos, Krisztian A1 - Horvath, Peter T1 - Segmentation metric misinterpretations in bioimage analysis JF - Nature Methods N2 - Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled. UR - https://doi.org/10.1038/s41592-023-01942-8 Y1 - 2023 UR - https://doi.org/10.1038/s41592-023-01942-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40491 SN - 1548-7091 SN - 1548-7105 VL - 21 IS - 2 SP - 213 EP - 216 PB - Springer Nature CY - Berlin ER - TY - JOUR A1 - Sievert, Matti A1 - Mantsopoulos, Konstantinos A1 - Müller, Sarina K. A1 - Eckstein, Markus A1 - Rupp, Robin A1 - Aubreville, Marc A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Systematic interpretation of confocal laser endomicroscopy: larynx and pharynx confocal imaging score JF - Acta otorhinolaryngologica italica N2 - Objective. Development and validation of a confocal laser endomicroscopy (CLE) classification score for the larynx and pharynx. Methods. Thirteen patients (154 video sequences, 9240 images) with laryngeal or pharyngeal SCC were included in this prospective study between October 2020 and February 2021. Each CLE sequence was correlated with the gold standard of histopathological examination. Based on a dataset of 94 video sequences (5640 images), a scoring system was developed. In the remaining 60 sequences (3600 images), the score was validated by four CLE experts and four head and neck surgeons who were not familiar with CLE. Results. Tissue homogeneity, cell size, borders and clusters, capillary loops and the nucleus/ cytoplasm ratio were defined as the scoring criteria. Using this score, the CLE experts obtained an accuracy, sensitivity, and specificity of 90.8%, 95.1%, and 86.4%, respectively, and the CLE non-experts of 86.2%, 86.4%, and 86.1%. Interobserver agreement Fleiss’ kappa was 0.8 and 0.6, respectively. Conclusions. CLE can be reliably evaluated based on defined and reproducible imaging features, which demonstrate a high diagnostic value. CLE can be easily integrated into the intraoperative setting and generate real-time, in-vivo microscopic images to demarcate malignant changes. UR - https://doi.org/10.14639/0392-100X-N1643 KW - confocal laser endomicroscopy KW - head and neck cancer KW - classification system KW - non-invasive histological imaging KW - larynx KW - pharynx Y1 - 2022 UR - https://doi.org/10.14639/0392-100X-N1643 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29175 SN - 1827-675X VL - 42 IS - 1 SP - 26 EP - 33 PB - Pacini CY - Pisa ER - TY - CHAP A1 - Qiu, Jingna A1 - Wilm, Frauke A1 - Öttl, Mathias A1 - Schlereth, Maja A1 - Liu, Chang A1 - Heimann, Tobias A1 - Aubreville, Marc A1 - Breininger, Katharina ED - Greenspan, Hayit ED - Madabhushi, Anant ED - Mousavi, Parvin ED - Salcudean, Septimiu ED - Duncan, James ED - Syeda-Mahmood, Tanveer ED - Taylor, Russell T1 - Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation T2 - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: Proceedings, Part II UR - https://doi.org/10.1007/978-3-031-43895-0_9 Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-43895-0_9 SN - 978-3-031-43895-0 SN - 1611-3349 SP - 90 EP - 100 PB - Springer CY - Cham ER - TY - CHAP A1 - Ganz, Jonathan A1 - Lipnik, Karoline A1 - Ammeling, Jonas A1 - Richter, Barbara A1 - Puget, Chloé A1 - Parlak, Eda A1 - Diehl, Laura A1 - Klopfleisch, Robert A1 - Donovan, Taryn A1 - Kiupel, Matti A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images T2 - Bildverarbeitung für die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, Braunschweig, July 2-4, 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_49 Y1 - 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_49 SN - 978-3-658-41657-7 SN - 978-3-658-41656-0 SP - 226 EP - 231 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Wilm, Frauke A1 - Fragoso-Garcia, Marco A1 - Bertram, Christof A1 - Stathonikos, Nikolas A1 - Öttl, Mathias A1 - Qiu, Jingna A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology T2 - 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI53787.2023.10230458 KW - Histopathology KW - Domain Shift KW - Representation Learning KW - Barlow Twins Y1 - 2023 UR - https://doi.org/10.1109/ISBI53787.2023.10230458 SN - 978-1-6654-7358-3 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Aubreville, Marc A1 - Stathonikos, Nikolas A1 - Donovan, Taryn A1 - Klopfleisch, Robert A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Wilm, Frauke A1 - Veta, Mitko A1 - Jabari, Samir A1 - Eckstein, Markus A1 - Annuscheit, Jonas A1 - Krumnow, Christian A1 - Bozaba, Engin A1 - Cayir, Sercan A1 - Gu, Hongyan A1 - Chen, Xiang A1 - Jahanifar, Mostafa A1 - Shephard, Adam A1 - Kondo, Satoshi A1 - Kasai, Satoshi A1 - Kotte, Sujatha A1 - Saipradeep, Vangala A1 - Lafarge, Maxime W. A1 - Koelzer, Viktor H. A1 - Wang, Ziyue A1 - Zhang, Yongbing A1 - Yang, Sen A1 - Wang, Xiyue A1 - Breininger, Katharina A1 - Bertram, Christof T1 - Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge JF - Medical Image Analysis N2 - Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert majority vote and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today’s deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, with only minor changes in the order of participants in the ranking. UR - https://doi.org/10.1016/j.media.2024.103155 Y1 - 2024 UR - https://doi.org/10.1016/j.media.2024.103155 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58479 SN - 1361-8423 VL - 2024 IS - 94 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Ganz, Jonathan A1 - Marzahl, Christian A1 - Ammeling, Jonas A1 - Rosbach, Emely A1 - Richter, Barbara A1 - Puget, Chloé A1 - Denk, Daniela A1 - Demeter, Elena A. A1 - Tabaran, Flaviu A. A1 - Wasinger, Gabriel A1 - Lipnik, Karoline A1 - Tecilla, Marco A1 - Valentine, Matthew J. A1 - Dark, Michael A1 - Abele, Niklas A1 - Bolfa, Pompei A1 - Erber, Ramona A1 - Klopfleisch, Robert A1 - Merz, Sophie A1 - Donovan, Taryn A1 - Jabari, Samir A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis JF - Scientific Reports N2 - The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure. UR - https://doi.org/10.1038/s41598-024-77244-6 Y1 - 2024 UR - https://doi.org/10.1038/s41598-024-77244-6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53559 SN - 2045-2322 VL - 14 IS - 1 PB - Springer Nature CY - London ER - TY - JOUR A1 - Haghofer, Andreas A1 - Parlak, Eda A1 - Bartel, Alexander A1 - Donovan, Taryn A1 - Assenmacher, Charles-Antoine A1 - Bolfa, Pompei A1 - Dark, Michael A1 - Fuchs-Baumgartinger, Andrea A1 - Klang, Andrea A1 - Jäger, Kathrin A1 - Klopfleisch, Robert A1 - Merz, Sophie A1 - Richter, Barbara A1 - Schulman, F. Yvonne A1 - Janout, Hannah A1 - Ganz, Jonathan A1 - Scharinger, Josef A1 - Aubreville, Marc A1 - Winkler, Stephan M. A1 - Kiupel, Matti A1 - Bertram, Christof T1 - Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry JF - Veterinary Pathology N2 - Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning–based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. As compared with gold standard manual morphometry (area under the ROC curve [AUC] = 0.839, 95% confidence interval [CI] = 0.701–0.977), the prognostic value (tumor-specific survival) of SDs of nuclear area for practicable manual morphometry and automated morphometry were high with an AUC of 0.868 (95% CI = 0.737–0.991) and 0.943 (95% CI = 0.889–0.996), respectively. This study supports the use of manual morphometry with stratified sampling of 12 nuclei and algorithmic morphometry to overcome the poor reproducibility of estimates. Further studies are needed to validate our findings, determine inter-algorithmic reproducibility and algorithmic robustness, and explore tumor heterogeneity of nuclear features in entire tumor sections. UR - https://doi.org/10.1177/03009858241295399 Y1 - 2024 UR - https://doi.org/10.1177/03009858241295399 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53661 SN - 1544-2217 SN - 0300-9858 VL - 62 IS - 2 SP - 161 EP - 177 PB - Sage CY - London ER - TY - JOUR A1 - Stathonikos, Nikolas A1 - Aubreville, Marc A1 - de Vries, Sjoerd A1 - Wilm, Frauke A1 - Bertram, Christof A1 - Veta, Mitko A1 - van Diest, Paul J T1 - Breast cancer survival prediction using an automated mitosis detection pipeline JF - The Journal of Pathology: Clinical Research N2 - AbstractMitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter‐ and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)‐supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long‐term follow‐up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI‐based MC and light‐microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni‐ and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light‐microscopic MC. UR - https://doi.org/10.1002/2056-4538.70008 Y1 - 2024 UR - https://doi.org/10.1002/2056-4538.70008 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53087 SN - 2056-4538 VL - 10 IS - 6 PB - Wiley CY - Chichester ER - TY - INPR A1 - Qiu, Jingna A1 - Aubreville, Marc A1 - Wilm, Frauke A1 - Öttl, Mathias A1 - Utz, Jonas A1 - Schlereth, Maja A1 - Breininger, Katharina T1 - Leveraging Image Captions for Selective Whole Slide Image Annotation UR - https://doi.org/10.48550/arXiv.2407.06363 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2407.06363 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Glahn, Imaine A1 - Haghofer, Andreas A1 - Donovan, Taryn A1 - Degasperi, Brigitte A1 - Bartel, Alexander A1 - Kreilmeier-Berger, Theresa A1 - Hyndman, Philip S. A1 - Janout, Hannah A1 - Assenmacher, Charles-Antoine A1 - Bartenschlager, Florian A1 - Bolfa, Pompei A1 - Dark, Michael A1 - Klang, Andrea A1 - Klopfleisch, Robert A1 - Merz, Sophie A1 - Richter, Barbara A1 - Schulman, F. Yvonne A1 - Ganz, Jonathan A1 - Scharinger, Josef A1 - Aubreville, Marc A1 - Winkler, Stephan M. A1 - Bertram, Christof T1 - Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility JF - Veterinary Sciences N2 - The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required. UR - https://doi.org/10.3390/vetsci11060278 Y1 - 2024 UR - https://doi.org/10.3390/vetsci11060278 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48612 SN - 2306-7381 VL - 11 IS - 6 PB - MDPI CY - Basel ER - TY - JOUR A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Jabari, Samir A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Re-identification from histopathology images JF - Medical Image Analysis N2 - In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy. In addition, we compared a comprehensive set of state-of-the-art whole slide image classifiers and feature extractors for the given task. We evaluated our algorithms on two TCIA datasets including lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). We also demonstrate the algorithm’s performance on an in-house dataset of meningioma tissue. We predicted the source patient of a slide with 𝐹1 scores of up to 80.1% and 77.19% on the LSCC and LUAD datasets, respectively, and with 77.09% on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient’s privacy prior to publication. UR - https://doi.org/10.1016/j.media.2024.103335 Y1 - 2024 UR - https://doi.org/10.1016/j.media.2024.103335 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53025 SN - 1361-8423 SN - 1361-8415 VL - 2025 IS - 99 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Aubreville, Marc A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Rosbach, Emely A1 - Gehrke, Thomas A1 - Scherzad, Agmal A1 - Hackenberg, Stephan A1 - Goncalves, Miguel T1 - Prediction of tumor board procedural recommendations using large language models JF - European Archives of Oto-Rhino-Laryngology UR - https://doi.org/10.1007/s00405-024-08947-9 Y1 - 2024 UR - https://doi.org/10.1007/s00405-024-08947-9 SN - 1434-4726 SN - 0937-4477 VL - 282 IS - 3 SP - 1619 EP - 1629 PB - Springer CY - Berlin ER - TY - JOUR A1 - Frenken, Ann‐Kathrin A1 - Sievert, Matti A1 - Panuganti, Bharat A1 - Aubreville, Marc A1 - Meyer, Till A1 - Scherzad, Agmal A1 - Gehrke, Thomas A1 - Scheich, Matthias A1 - Hackenberg, Stephan A1 - Goncalves, Miguel T1 - Feasibility of Optical Biopsy During Endoscopic Sinus Surgery With Confocal Laser Endomicroscopy: A Pilot Study JF - The Laryngoscope UR - https://doi.org/10.1002/lary.31503 Y1 - 2024 UR - https://doi.org/10.1002/lary.31503 SN - 1531-4995 VL - 134 IS - 10 SP - 4217 EP - 4224 PB - Wiley CY - Malden ER - TY - CHAP A1 - Ammeling, Jonas A1 - Hecker, Moritz A1 - Ganz, Jonathan A1 - Donovan, Taryn A1 - Klopfleisch, Robert A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Maier, Andreas ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Automated Mitotic Index Calculation via Deep Learning and Immunohistochemistry T2 - Bildverarbeitung für die Medizin 2024: Proceedings, German Conference on Medical Image Computing, Erlangen, March 10–12, 2024 UR - https://doi.org/10.1007/978-3-658-44037-4_37 Y1 - 2024 UR - https://doi.org/10.1007/978-3-658-44037-4_37 SN - 978-3-658-44037-4 SP - 123 EP - 128 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Sievert, Matti A1 - Aubreville, Marc A1 - Mueller, Sarina Katrin A1 - Eckstein, Markus A1 - Breininger, Katharina A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision JF - European Archives of Oto-Rhino-Laryngology UR - https://doi.org/10.1007/s00405-024-08476-5 Y1 - 2024 UR - https://doi.org/10.1007/s00405-024-08476-5 SN - 1434-4726 SN - 0937-4477 VL - 281 IS - 4 SP - 2115 EP - 2122 PB - Springer CY - Berlin ER - TY - JOUR A1 - Aubreville, Marc T1 - Developing Robust AI Applications for Clinical Use: The Special Case of Pathology JF - Trillium Pathology UR - https://doi.org/10.47184/tp.2024.01.04 Y1 - 2024 UR - https://doi.org/10.47184/tp.2024.01.04 SN - 2752-1915 VL - 3 IS - 1 SP - 20 EP - 22 PB - Trillium GmbH Medizinischer Fachverlag CY - Grafrath ER - TY - CHAP A1 - Pernias, Pablo A1 - Rampas, Dominic A1 - Richter, Mats Leon A1 - Pal, Christopher A1 - Aubreville, Marc T1 - Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models T2 - The Twelfth International Conference on Learning Representations (ICLR 2024) Y1 - 2024 UR - https://openreview.net/forum?id=gU58d5QeGv PB - OpenReview ER - TY - CHAP A1 - Eisenmann, Matthias A1 - Reinke, Annika A1 - Weru, Vivienn A1 - Tizabi, Minu Dietlinde A1 - Isensee, Fabian A1 - Adler, Tim J. A1 - Ali, Sharib A1 - Andrearczyk, Vincent A1 - Aubreville, Marc A1 - Baid, Ujjwal A1 - Bakas, Spyridon A1 - Balu, Niranjan A1 - Bano, Sophia A1 - Bernal, Jorge A1 - Bodenstedt, Sebastian A1 - Casella, Alessandro A1 - Cheplygina, Veronika A1 - Daum, Marie A1 - De Bruijne, Marleen A1 - Depeursinge, Adrien A1 - Dorent, Reuben A1 - Egger, Jan A1 - Ellis, David G. A1 - Engelhardt, Sandy A1 - Ganz, Melanie A1 - Ghatwary, Noha M. A1 - Girard, Gabriel A1 - Godau, Patrick A1 - Gupta, Anubha A1 - Hansen, Lasse A1 - Harada, Kanako A1 - Heinrich, Mattias A1 - Heller, Nicholas A1 - Hering, Alessa A1 - Huaulmé, Arnoud A1 - Jannin, Pierre A1 - Kavur, A. Emre A1 - Kodym, Oldrich A1 - Kozubek, Michal A1 - Li, Jianning A1 - Li, Hongwei A1 - Ma, Jun A1 - Martín-Isla, Carlos A1 - Menze, Bjoern H. A1 - Noble, Alison A1 - Oreiller, Valentin A1 - Padoy, Nicolas A1 - Pati, Sarthak A1 - Payette, Kelly A1 - Rädsch, Tim A1 - Rafael-Patiño, Jonathan A1 - Bawa, Vivek Singh A1 - Speidel, Stefanie A1 - Sudre, Carole H. A1 - Van Wijnen, Kimberlin M. H. A1 - Wagner, M. A1 - Wei, D. A1 - Yamlahi, Amine A1 - Yap, Moi Hoon A1 - Yuan, C. A1 - Zenk, Maximilian A1 - Zia, A. A1 - Zimmerer, David A1 - Aydogan, Dogu Baran A1 - Bhattarai, B. A1 - Bloch, Louise A1 - Brüngel, Raphael A1 - Cho, J. A1 - Choi, C. A1 - Dou, Q. A1 - Ezhov, Ivan A1 - Friedrich, Christoph M. A1 - Fuller, C. A1 - Gaire, Rebati Raman A1 - Galdran, Adrian A1 - García-Faura, Álvaro A1 - Grammatikopoulou, Maria A1 - Hong, S. A1 - Jahanifar, Mostafa A1 - Jang, I. A1 - Kadkhodamohammadi, Abdolrahim A1 - Kang, I. A1 - Kofler, Florian A1 - Kondo, Satoshi A1 - Kuijf, Hugo Jaco A1 - Li, M. A1 - Luu, M. A1 - Martinčič, Tomaz A1 - Morais, P. A1 - Naser, M. A. A1 - Oliveira, B. A1 - Owen, D. A1 - Pang, S. A1 - Park, Jinah A1 - Park, S. A1 - Płotka, S. A1 - Puybareau, Élodie A1 - Rajpoot, Nasir M. A1 - Ryu, K. A1 - Saeed, N. A1 - Shephard, Adam A1 - Shi, P. A1 - Štepec, Dejan A1 - Subedi, Ronast A1 - Tochon, Guillaume A1 - Torres, Helena R. A1 - Urien, Hélène A1 - Vilaça, João L. A1 - Wahid, Kareem A. A1 - Wang, H. A1 - Wang, J. A1 - Wang, L. A1 - Wang, Xiyue A1 - Wiestler, Benedikt A1 - Wodzinski, Marek A1 - Xia, F. A1 - Xie, J. A1 - Xiong, Z. A1 - Yang, Sen A1 - Yang, Y. A1 - Zhao, Z. A1 - Maier-Hein, Klaus H. A1 - Jäger, Paul F. A1 - Kopp-Schneider, Annette A1 - Maier-Hein, Lena T1 - Why is the Winner the Best? T2 - Proceedings: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition UR - https://doi.org/10.1109/CVPR52729.2023.01911 KW - Medical and biological vision KW - cell microscopy Y1 - 2023 UR - https://doi.org/10.1109/CVPR52729.2023.01911 SN - 979-8-3503-0129-8 SN - 2575-7075 SP - 19955 EP - 19967 PB - IEEE CY - Los Alamitos ER - TY - JOUR A1 - Wilm, Frauke A1 - Ihling, Christian A1 - Méhes, Gábor A1 - Terracciano, Luigi A1 - Puget, Chloé A1 - Klopfleisch, Robert A1 - Schüffler, Peter A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Mrowiec, Thomas A1 - Breininger, Katharina T1 - Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry JF - Journal of Pathology Informatics N2 - The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor microenvironment. An in-depth understanding of the spatial localization of immune cells is therefore critical for understanding the tumor’s immune landscape and predicting drug response. Computer-aided systems are well suited for efficiently quantifying immune cells in their spatial context. Conventional image analysis approaches are often based on color features and therefore require a high level of manual interaction. More robust image analysis methods based on deep learning are expected to decrease this reliance on human interaction and improve the reproducibility of immune cell scoring. However, these methods require sufficient training data and previous work has reported low robustness of these algorithms when they are tested on out-of-distribution data from different pathology labs or samples from different organs. In this work, we used a new image analysis pipeline to explicitly evaluate the robustness of marker-labeled lymphocyte quantification algorithms depending on the number of training samples before and after being transferred to a new tumor indication. For these experiments, we adapted the RetinaNet architecture for the task of T-lymphocyte detection and employed transfer learning to bridge the domain gap between tumor indications and reduce the annotation costs for unseen domains. On our test set, we achieved human-level performance for almost all tumor indications with an average precision of 0.74 in-domain and 0.72–0.74 cross-domain. From our results, we derive recommendations for model development regarding annotation extent, training sample selection, and label extraction for the development of robust algorithms for immune cell scoring. By extending the task of marker-labeled lymphocyte quantification to a multi-class detection task, the pre-requisite for subsequent analyses, e.g., distinguishing lymphocytes in the tumor stroma from tumor-infiltrating lymphocytes, is met. UR - https://doi.org/10.1016/j.jpi.2023.100301 KW - Tumor-infiltrating lymphocytes KW - Immuno-oncology KW - Immunohistochemistry KW - Deep learning KW - Transfer learning KW - Domain adaptation Y1 - 2023 UR - https://doi.org/10.1016/j.jpi.2023.100301 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40458 SN - 2153-3539 VL - 2023 IS - 14 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Fragoso-Garcia, Marco A1 - Wilm, Frauke A1 - Bertram, Christof A1 - Merz, Sophie A1 - Schmidt, Anja A1 - Donovan, Taryn A1 - Fuchs-Baumgartinger, Andrea A1 - Bartel, Alexander A1 - Marzahl, Christian A1 - Diehl, Laura A1 - Puget, Chloe A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina A1 - Klopfleisch, Robert T1 - Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images JF - Veterinary Pathology N2 - Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation ( n = 35 WSIs), and test sets ( n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types. UR - https://doi.org/10.1177/03009858231189205 KW - computer-aided diagnosis KW - computational pathology KW - digital pathology KW - dog KW - machine learning KW - skin KW - veterinary oncology Y1 - 2023 UR - https://doi.org/10.1177/03009858231189205 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38321 SN - 0300-9858 VL - 60 IS - 6 SP - 865 EP - 875 PB - Sage CY - London ER - TY - CHAP A1 - Ammeling, Jonas A1 - Manger, Carina A1 - Kwaka, Elias A1 - Krügel, Sebastian A1 - Uhl, Matthias A1 - Kießig, Angelika A1 - Fritz, Alexis A1 - Ganz, Jonathan A1 - Riener, Andreas A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Stolze, Markus ED - Loch, Frieder ED - Baldauf, Matthias ED - Alt, Florian ED - Schneegass, Christina ED - Kosch, Thomas ED - Hirzle, Teresa ED - Sadeghian, Shadan ED - Draxler, Fiona ED - Bektas, Kenan ED - Lohan, Katrin ED - Knierim, Pascal T1 - Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making T2 - Mensch und Computer 2023: Building Bridges: Tagungsband (Proceedings) UR - https://doi.org/10.1145/3603555.3608561 Y1 - 2023 UR - https://doi.org/10.1145/3603555.3608561 SN - 979-8-4007-0771-1 SP - 330 EP - 335 PB - ACM CY - New York ER - TY - JOUR A1 - Krügel, Sebastian A1 - Ammeling, Jonas A1 - Aubreville, Marc A1 - Fritz, Alexis A1 - Kießig, Angelika A1 - Uhl, Matthias T1 - Perceived responsibility in AI-supported medicine JF - AI & Society: Journal of Knowledge, Culture and Communication N2 - In a representative vignette study in Germany with 1,653 respondents, we investigated laypeople’s attribution of moral responsibility in collaborative medical diagnosis. Specifically, we compare people’s judgments in a setting in which physicians are supported by an AI-based recommender system to a setting in which they are supported by a human colleague. It turns out that people tend to attribute moral responsibility to the artificial agent, although this is traditionally considered a category mistake in normative ethics. This tendency is stronger when people believe that AI may become conscious at some point. In consequence, less responsibility is attributed to human agents in settings with hybrid diagnostic teams than in settings with human-only diagnostic teams. Our findings may have implications for behavior exhibited in contexts of collaborative medical decision making with AI-based as opposed to human recommenders because less responsibility is attributed to agents who have the mental capacity to care about outcomes. UR - https://doi.org/10.1007/s00146-024-01972-6 Y1 - 2024 UR - https://doi.org/10.1007/s00146-024-01972-6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57874 SN - 1435-5655 VL - 40 SP - 1485 EP - 1495 PB - Springer CY - London ER - TY - CHAP A1 - Ammeling, Jonas A1 - Wilm, Frauke A1 - Ganz, Jonathan A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Sheng, Bin ED - Aubreville, Marc T1 - Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge T2 - Mitosis Domain Generalization and Diabetic Retinopathy Analysis UR - https://doi.org/10.1007/978-3-031-33658-4_19 Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-33658-4_19 SN - 978-3-031-33658-4 SN - 978-3-031-33657-7 SP - 201 EP - 205 PB - Springer CY - Cham ER - TY - JOUR A1 - Sievert, Matti A1 - Aubreville, Marc A1 - Gostian, Antoniu-Oreste A1 - Mantsopoulos, Konstantinos A1 - Koch, Michael A1 - Müller, Sarina K. A1 - Eckstein, Markus A1 - Rupp, Robin A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Validity of tissue homogeneity in confocal laser endomicroscopy on the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose Confocal laser endomicroscopy (CLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. This study analyzes differences in tissue homogeneity between healthy mucosa and squamous cell carcinoma (SCC) via CLE. Materials and methods We included five SCC patients with planned total laryngectomy in this study between October 2020 and February 2021. We captured CLE scans of the tumor and healthy mucosa. Analysis of image homogeneity to diagnose SCC was performed by measuring the signal intensity in four regions of interest (ROI) in each frame in a total of 60 sequences. Each sequence was assigned to the corresponding histological pattern, derived from hematoxylin and eosin staining. In addition, we recorded the subjective evaluation of seven investigators regarding tissue homogeneity. Results Out of 3600 images, 1620 (45%) correlated with benign mucosa and 1980 (55%) with SCC. ROIs of benign mucosa and SCC had a mean and standard deviation (SD) of signal intensity of, respectively, 232.1 ± 3.34 and 467.3 ± 9.72 (P < 0.001). The mean SD between the four different ROIs was 39.1 ± 1.03 for benign and 101.5 ± 2.6 for SCC frames (P < 0.001). In addition, homogeneity yielded a sensitivity and specificity of 81.8% and 86.2%, respectively, regarding the investigator-dependent analysis. Conclusions SCC shows a significant tissue inhomogeneity in comparison to the healthy epithelium. The results support this feature’s importance in identifying malignant mucosa areas during CLE examination. However, the examiner-dependent evaluation emphasizes that homogeneity is a sub-criterion that must be considered in a broad context. UR - https://doi.org/10.1007/s00405-022-07304-y KW - confocal laser endomicroscopy KW - head and neck cancer KW - classification system KW - non-invasive histological imaging KW - larynx KW - pharynx Y1 - 2022 UR - https://doi.org/10.1007/s00405-022-07304-y UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-23180 SN - 1434-4726 SN - 0937-4477 VL - 279 IS - 8 SP - 4147 EP - 4156 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Sievert, Matti A1 - Mantsopoulos, Konstantinos A1 - Müller, Sarina K. A1 - Rupp, Robin A1 - Eckstein, Markus A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy JF - Brazilian Journal of Otorhinolaryngology N2 - Introduction Confocal laser endomicroscopy is an optical imaging technique that allows in vivo, real-time, microscope-like images of the upper aerodigestive tract’s mucosa. The assessment of morphological tissue characteristics for the correct differentiation between healthy and malignant suspected mucosa requires strict evaluation criteria. Objective This study aims to validate an eight-point score for the correct assessment of malignancy. Methods We performed confocal laser endomicroscopy between March and October 2020 in 13 patients. 197 sequences (11.820 images) originated from the marginal area of pharyngeal and laryngeal carcinomas. Specimens were taken at corresponding locations and analyzed in H&E staining as a standard of reference. A total of six examiners evaluated the sequences based on a scoring system; they were blinded to the histopathological examination. The primary endpoints are sensitivity, specificity, and accuracy. Secondary endpoints are interrater reliability and receiver operator characteristics. Results Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. Confocal laser endomicroscopy of malignant cells demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 83.2%, 81.3%, 85.5%, 86.7%, and 79.7%, respectively, with a κ-value of 0.64, and an area under the curve of 0.86. Conclusion The results confirm that this scoring system is applicable in the laryngeal and pharyngeal mucosa to classify benign and malignant tissue. A scoring system based on defined and reproducible characteristics can help translate this experimental method to broad clinical practice in head and neck diagnosis. UR - https://doi.org/10.1016/j.bjorl.2021.06.002 KW - confocal laser endomicroscopy KW - squamous cell carcinoma KW - head and neck cancer KW - diagnosis Y1 - 2021 UR - https://doi.org/10.1016/j.bjorl.2021.06.002 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-20887 SN - 1808-8694 VL - 2022 IS - 88, S4 SP - 26 EP - 32 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Sievert, Matti A1 - Eckstein, Markus A1 - Mantsopoulos, Konstantinos A1 - Müller, Sarina K. A1 - Stelzle, Florian A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE. Materials and methods We included ten patients with confirmed SCC and planned total laryngectomy in this study between March 2020 and February 2021. CLE images of these patients were collected and compared with the corresponding histology in hematoxylin and eosin staining. We analyzed the characteristic endomicroscopic patterns of blood vessels and intraepithelial capillary loops for the diagnosis of SCC. Results In a total of 54 sequences, we identified 243 blood vessels which were analyzed regarding structure, diameter, and Fluorescein leakage, confirming that irregular, corkscrew-like vessels (24.4% vs. 1.3%; P < .001), dilated intraepithelial capillary loops (90.8% vs. 28.7%; P < .001), and increased capillary leakage (40.7% vs. 2.5%; P < .001), are significantly more frequently detected in SCC compared to the healthy epithelium. We defined a vessel diameter of 30 μm in capillary loops as a cut-off value, obtaining a sensitivity, specificity, PPV, and NPV and accuracy of 90.6%, 71.3%, 57.4%, 94.7%, and 77.1%, respectively, for the detection of malignancy based solely on capillary architecture. Conclusion Capillaries within malignant lesions are fundamentally different from those in healthy mucosa regions. The capillary architecture is a significant feature aiding the identification of malignant mucosa areas during in-vivo, real-time CLE examination. UR - https://doi.org/10.1007/s00405-021-06954-8 KW - confocal laser endomicroscopy KW - head and neck squamous cell carcinoma KW - capillary loops KW - malignant vascularization KW - head and neck malignancies Y1 - 2021 UR - https://doi.org/10.1007/s00405-021-06954-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-23196 SN - 1434-4726 SN - 0937-4477 VL - 279 IS - 4 SP - 2029 EP - 2037 PB - Springer Nature CY - Cham ER - TY - CHAP A1 - Aubreville, Marc A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Donovan, Taryn A1 - Fick, Rutger H. J. A1 - Breininger, Katharina A1 - Bertram, Christof ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Deep Learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-free Object Detector T2 - Bildverarbeitung für die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, Braunschweig, July 2-4, 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_40 Y1 - 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_40 SN - 978-3-658-41657-7 SN - 978-3-658-41656-0 SP - 189 EP - 195 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Aubreville, Marc ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation T2 - Bildverarbeitung für die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, Braunschweig, July 2-4, 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_27 Y1 - 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_27 SN - 978-3-658-41657-7 SN - 978-3-658-41656-0 SP - 116 EP - 121 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Aubreville, Marc A1 - Stathonikos, Nikolas A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Hoeve, Natalie ter A1 - Ciompi, Francesco A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Donovan, Taryn A1 - Maier, Andreas A1 - Breen, Jack A1 - Ravikumar, Nishant A1 - Chung, Youjin A1 - Park, Jinah A1 - Nateghi, Ramin A1 - Pourakpour, Fattaneh A1 - Fick, Rutger H. J. A1 - Ben Hadj, Saima A1 - Jahanifar, Mostafa A1 - Shepard, Adam A1 - Dexl, Jakob A1 - Wittenberg, Thomas A1 - Kondo, Satoshi A1 - Lafarge, Maxime W. A1 - Kolezer, Viktor H. A1 - Liang, Jingtang A1 - Wang, Yubo A1 - Long, Xi A1 - Liu, Jingxin A1 - Razavi, Salar A1 - Khademi, April A1 - Yang, Sen A1 - Wang, Xiyue A1 - Erber, Ramona A1 - Klang, Andrea A1 - Lipnik, Karoline A1 - Bolfa, Pompei A1 - Dark, Michael A1 - Wasinger, Gabriel A1 - Veta, Mitko A1 - Breininger, Katharina T1 - Mitosis domain generalization in histopathology images — The MIDOG challenge JF - Medical Image Analysis UR - https://doi.org/10.1016/j.media.2022.102699 KW - Domain generalization KW - Histopathology KW - Challenge KW - Deep Learning KW - Mitosis Y1 - 2022 UR - https://doi.org/10.1016/j.media.2022.102699 SN - 1361-8423 SN - 1361-8415 VL - 2023 IS - 84 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Pan, Zhaoya A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Mantsopoulos, Konstantinos A1 - Iro, Heinrich A1 - Goncalves, Miguel A1 - Sievert, Matti T1 - Defining a baseline identification of artifacts in confocal laser endomicroscopy in head and neck cancer imaging JF - American Journal of Otolaryngology UR - https://doi.org/10.1016/j.amjoto.2022.103779 KW - Confocal laser endomicroscopy KW - Head and neck cancer KW - Classification system KW - Non-invasive histological imaging KW - Larynx KW - Pharynx Y1 - 2022 UR - https://doi.org/10.1016/j.amjoto.2022.103779 SN - 1532-818X SN - 0196-0709 VL - 44 IS - 2 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Theelke, Luisa A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Iterative Cross-Scanner Registration for Whole Slide Images T2 - 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) UR - https://doi.org/10.1109/ICCVW54120.2021.00071 KW - Pathology KW - Computer vision KW - Image resolution KW - Microscopy KW - Conferences KW - Estimation KW - Registers Y1 - 2021 UR - https://doi.org/10.1109/ICCVW54120.2021.00071 SN - 978-1-6654-0191-3 SN - 2473-9944 N1 - Eine frei zugängliche Version ist veröffentlicht unter: https://openaccess.thecvf.com/content/ICCV2021W/CDPath/html/Theelke_Iterative_Cross-Scanner_Registration_for_Whole_Slide_Images_ICCVW_2021_paper.html SP - 582 EP - 590 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Stathonikos, Nikolas A1 - ter Hoeve, Natalie A1 - Ciompi, Francesco A1 - Klopfleisch, Robert A1 - Veta, Mitko A1 - Donovan, Taryn A1 - Marzahl, Christian A1 - Wilm, Frauke A1 - Breininger, Katharina A1 - Maier, Andreas T1 - Quantifying the Scanner-Induced Domain Gap in Mitosis Detection T2 - MIDL: Medical Imaging with Deep Learning 2021 Y1 - 2021 UR - https://2021.midl.io/papers/i6 PB - MIDL Foundation CY - Nijmegen ER - TY - CHAP A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Aubreville, Marc ED - Zimmerer, David ED - Heinrich, Mattias T1 - Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge T2 - Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis : MICCAI 2021 Challenges UR - https://doi.org/10.1007/978-3-030-97281-3_1 Y1 - 2022 UR - https://doi.org/10.1007/978-3-030-97281-3_1 SN - 978-3-030-97281-3 SP - 5 EP - 13 PB - Springer CY - Cham ER - TY - JOUR A1 - Bertram, Christof A1 - Marzahl, Christian A1 - Bartel, Alexander A1 - Stayt, Jason A1 - Bonsembiante, Federico A1 - Beeler-Marfisi, Janet A1 - Barton, Ann K. A1 - Brocca, Ginevra A1 - Gelain, Maria Elena A1 - Gläsel, Agnes C. A1 - du Preez, Kelly A1 - Weiler, Kristina A1 - Weissenbacher-Lang, Christiane A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Hill, Jenny T1 - Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm JF - Veterinary Pathology N2 - Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis. UR - https://doi.org/10.1177/03009858221137582 KW - artificial intelligence KW - automated image analysis KW - bronchoalveolar lavage fluid KW - computational pathology KW - digital pathology KW - equine KW - pulmonary hemorrhage KW - respiratory disease KW - total hemosiderin score Y1 - 2022 UR - https://doi.org/10.1177/03009858221137582 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-35196 SN - 1544-2217 VL - 60 IS - 1 SP - 75 EP - 85 PB - Sage CY - London ER - TY - JOUR A1 - Marzahl, Christian A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Maier, Jennifer A1 - Bergler, Christian A1 - Kröger, Christine A1 - Voigt, Jörn A1 - Breininger, Katharina A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control JF - Scientific Reports N2 - In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering. UR - https://doi.org/10.1038/s41598-021-83827-4 Y1 - 2021 UR - https://doi.org/10.1038/s41598-021-83827-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10943 SN - 2045-2322 VL - 11 PB - Springer Nature CY - London ER - TY - JOUR A1 - Sievert, Matti A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Stelzle, Florian A1 - Maier, Andreas A1 - Mantsopoulos, Konstantinos A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Konfokale Laser-Endomikroskopie des Kopf-Hals-Plattenepithelkarzinoms: eine systematische Übersicht JF - Laryngo-Rhino-Otologie T2 - Confocal laser endomicroscopy of head and neck squamous cell carcinoma: a systematic review UR - https://doi.org/10.1055/a-1339-1635 KW - konfokale Laser-Endomikroskopie KW - CLE KW - Kopf-Hals-Malignom KW - Larynxkarzinom KW - Bildanalyse KW - confocal laser endomicroscopy KW - head and neck malignancy KW - laryngeal carcinoma KW - image analysis Y1 - 2021 UR - https://doi.org/10.1055/a-1339-1635 SN - 0935-8943 VL - 100 IS - 11 SP - 875 EP - 881 PB - Thieme CY - Stuttgart ER - TY - JOUR A1 - Sievert, Matti A1 - Stelzle, Florian A1 - Aubreville, Marc A1 - Müller, Sarina K. A1 - Eckstein, Markus A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Mantsopoulos, Konstantinos A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery. Methods We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results. Results Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and κ-value of 0.60. Conclusion CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin. UR - https://doi.org/10.1007/s00405-021-06659-y KW - Confocal laser endomicroscopy KW - Oropharyngeal squamous cell carcinoma KW - Safe surgical margins KW - Head and neck malignancies Y1 - 2021 UR - https://doi.org/10.1007/s00405-021-06659-y UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10914 SN - 1434-4726 SN - 0937-4477 VL - 278 IS - 11 SP - 4433 EP - 4439 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Meuten, Donald J. A1 - Moore, Frances M. A1 - Donovan, Taryn A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Foster, Robert A. A1 - Smedley, Rebecca C. A1 - Dark, Michael A1 - Milovancev, Milan A1 - Stromberg, Paul A1 - Williams, Bruce H. A1 - Aubreville, Marc A1 - Avallone, Giancarlo A1 - Bolfa, Pompei A1 - Cullen, John A1 - Dennis, Michelle M. A1 - Goldschmidt, Michael A1 - Luong, Richard A1 - Miller, Andrew D. A1 - Miller, Margaret A. A1 - Munday, John S. A1 - Roccabianca, Paola A1 - Salas, Elisa N. A1 - Schulman, F. Yvonne A1 - Laufer-Amorim, Renee A1 - Asakawa, Midori G. A1 - Craig, Linden A1 - Dervisis, Nick A1 - Esplin, D. Glen A1 - George, Jeanne W. A1 - Hauck, Marlene A1 - Kagawa, Yumiko A1 - Kiupel, Matti A1 - Linder, Keith A1 - Meichner, Kristina A1 - Marconato, Laura A1 - Oblak, Michelle L. A1 - Santos, Renato L. A1 - Simpson, R. Mark A1 - Tvedten, Harold A1 - Whitley, Derick T1 - International Guidelines for Veterinary Tumor Pathology: A Call to Action JF - Veterinary Pathology UR - https://doi.org/10.1177/03009858211013712 KW - standardization KW - oncology KW - guidelines KW - protocols KW - validation Y1 - 2021 UR - https://doi.org/10.1177/03009858211013712 SN - 1544-2217 VL - 58 IS - 5 SP - 766 EP - 794 PB - Sage CY - London ER - TY - CHAP A1 - Wilm, Frauke A1 - Bertram, Christof A1 - Marzahl, Christian A1 - Bartel, Alexander A1 - Donovan, Taryn A1 - Assenmacher, Charles-Antoine A1 - Becker, Kathrin A1 - Bennett, Mark A1 - Corner, Sarah M. A1 - Cossic, Brieuc A1 - Denk, Daniela A1 - Dettwiler, Martina A1 - Garcia Gonzalez, Beatriz A1 - Gurtner, Corinne A1 - Heier, Annabelle A1 - Lehmbecker, Annika A1 - Merz, Sophie A1 - Plog, Stephanie A1 - Schmidt, Anja A1 - Sebastian, Franziska A1 - Smedley, Rebecca C. A1 - Tecilla, Marco A1 - Thaiwong, Tuddow A1 - Breininger, Katharina A1 - Kiupel, Matti A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Aubreville, Marc T1 - Influence of inter-annotator variability on automatic mitotic figure assessment T2 - Bildverarbeitung für die Medizin 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_56 Y1 - 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_56 SN - 978-3-658-33198-6 SP - 241 EP - 246 PB - Springer CY - Wiesbaden ER - TY - CHAP A1 - Bertram, Christof A1 - Donovan, Taryn A1 - Tecilla, Marco A1 - Bartenschlager, Florian A1 - Fragoso-Garcia, Marco A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Breininger, Katharina A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Aubreville, Marc T1 - Dataset on bi- and multi-nucleated tumor cells in canine cutaneous mast cell tumors T2 - Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7–9, 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_33 Y1 - 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_33 SN - 978-3-658-33197-9 SN - 978-3-658-33198-6 SN - 1431-472X SP - 134 EP - 139 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Wilm, Frauke A1 - Voigt, Jörn A1 - Barton, Ann K. A1 - Klopfleisch, Robert A1 - Breininger, Katharina A1 - Maier, Andreas A1 - Aubreville, Marc T1 - Cell detection for asthma on partially annotated whole slide images BT - learning to be EXACT T2 - Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7–9, 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_36 Y1 - 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_36 SN - 978-3-658-33197-9 SN - 978-3-658-33198-6 SN - 1431-472X SP - 147 EP - 152 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Wilm, Frauke A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Investigation of Class Separability within Object Detection Models in Histopathology JF - IEEE Transactions on Medical Imaging UR - https://doi.org/10.1109/TMI.2025.3560134 Y1 - 2025 UR - https://doi.org/10.1109/TMI.2025.3560134 SN - 0278-0062 SN - 1558-254X VL - 44 IS - 8 SP - 3162 EP - 3174 PB - IEEE CY - New York ER - TY - JOUR A1 - Oetter, Nicolai A1 - Pröll, Jonas A1 - Sievert, Matti A1 - Goncalves, Miguel A1 - Rohde, Maximilian A1 - Nobis, Christopher-Philipp A1 - Knipfer, Christian A1 - Aubreville, Marc A1 - Pan, Zhaoya A1 - Breininger, Katharina A1 - Maier, Andreas A1 - Kesting, Marco A1 - Stelzle, Florian T1 - Oral mucosa - an examination map for confocal laser endomicroscopy within the oral cavity: an experimental clinical study JF - Clinical Oral Investigations N2 - Objectives Confocal laser endomicroscopy (CLE) is an optical method that enables microscopic visualization of oral mucosa. Previous studies have shown that it is possible to differentiate between physiological and malignant oral mucosa. However, differences in mucosal architecture were not taken into account. The objective was to map the different oral mucosal morphologies and to establish a “CLE map” of physiological mucosa as baseline for further application of this powerful technology. Materials and methods The CLE database consisted of 27 patients. The following spots were examined: (1) upper lip (intraoral) (2) alveolar ridge (3) lateral tongue (4) floor of the mouth (5) hard palate (6) intercalary line. All sequences were examined by two CLE experts for morphological differences and video quality. Results Analysis revealed clear differences in image quality and possibility of depicting tissue morphologies between the various localizations of oral mucosa: imaging of the alveolar ridge and hard palate showed visually most discriminative tissue morphology. Labial mucosa was also visualized well using CLE. Here, typical morphological features such as uniform cells with regular intercellular gaps and vessels could be clearly depicted. Image generation and evaluation was particularly difficult in the area of the buccal mucosa, the lateral tongue and the floor of the mouth. Conclusion A physiological “CLE map” for the entire oral cavity could be created for the first time. Clinical relevance This will make it possible to take into account the existing physiological morphological features when differentiating between normal mucosa and oral squamous cell carcinoma in future work. UR - https://doi.org/10.1007/s00784-024-05664-9 KW - Confocal laser endomicroscopy KW - Oral cavity KW - Oral carcinoma KW - Optical methods KW - Oral cavity squamous cell carcinoma KW - Head and neck cancer Y1 - 2024 UR - https://doi.org/10.1007/s00784-024-05664-9 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-56000 SN - 1436-3771 VL - 28 IS - 5 PB - Springer CY - Berlin ER - TY - JOUR A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Benchmarking Foundation Models for Mitotic Figure Classification JF - Machine Learning for Biomedical Imaging N2 - The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled images for a specific task is often limited. Self-supervised learning techniques have enabled the use of vast amounts of unlabeled data to train large-scale neural networks, i.e., foundation models, that can address the limited data problem by providing semantically rich feature vectors that can generalize well to new tasks with minimal training effort increasing model performance and robustness. In this work, we investigate the use of foundation models for mitotic figure classification. The mitotic count, which can be derived from this classification task, is an independent prognostic marker for specific tumors and part of certain tumor grading systems. In particular, we investigate the data scaling laws on multiple current foundation models and evaluate their robustness to unseen tumor domains. Next to the commonly used linear probing paradigm, we also adapt the models using low-rank adaptation (LoRA) of their attention mechanisms. We compare all models against end-to-end-trained baselines, both CNNs and Vision Transformers. Our results demonstrate that LoRA-adapted foundation models provide superior performance to those adapted with standard linear probing, reaching performance levels close to 100 % data availability with only 10 % of training data. Furthermore, LoRA-adaptation of the most recent foundation models almost closes the out-of-domain performance gap when evaluated on unseen tumor domains. However, full fine-tuning of traditional architectures still yields competitive performance. UR - https://doi.org/10.59275/j.melba.2026-a3eb Y1 - 2026 UR - https://doi.org/10.59275/j.melba.2026-a3eb UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66220 SN - 2766-905X VL - 3 IS - MELBA–BVM 2025 Special Issue SP - 38 EP - 55 PB - Melba editors CY - [s. l.] ER - TY - CHAP A1 - Rosbach, Emely A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Riener, Andreas A1 - Aubreville, Marc ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09–11, 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_27 Y1 - 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_27 SN - 978-3-658-47422-5 SP - 129 EP - 134 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - Is Self-supervision Enough? BT - Benchmarking Foundation Models Against End-to-end Training for Mitotic Figure Classification T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09–11, 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_15 Y1 - 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_15 SN - 978-3-658-47422-5 SP - 63 EP - 68 PB - Springer Vieweg CY - Wiesbaden ER - TY - INPR A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Benchmarking Foundation Models for Mitotic Figure Classification UR - https://doi.org/10.48550/arXiv.2508.04441 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2508.04441 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Puget, Chloé A1 - Ganz, Jonathan A1 - Bertram, Christof A1 - Conrad, Thomas A1 - Baeblich, Malte A1 - Voss, Anne A1 - Landmann, Katharina A1 - Haake, Alexander F. H. A1 - Spree, Andreas A1 - Hartung, Svenja A1 - Aeschlimann, Leonore A1 - Soto, Sara A1 - de Brot, Simone A1 - Dettwiler, Martina A1 - Aupperle-Lellbach, Heike A1 - Bolfa, Pompei A1 - Bartel, Alexander A1 - Kiupel, Matti A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Klopfleisch, Robert T1 - Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it? JF - Veterinary Pathology N2 - Canine cutaneous mast cell tumors (ccMCTs) are frequent neoplasms with variable biological behaviors. Internal tandem duplication mutations in c-KIT exon 11 (c-KIT-11-ITD) are associated with poor prognosis but predict therapeutic response to tyrosine kinase inhibitors. In a previous work, deep learning algorithms managed to predict the presence of c-KIT-11-ITD on digitalized hematoxylin and eosin-stained histological slides (whole-slide images, WSIs) in up to 87% of cases, suggesting the existence of morphological features characterizing ccMCTs carrying c-KIT-11-ITD. This 3-stage blinded study aimed to identify morphological features indicative of c-KIT-11-ITD and to evaluate the ability of human observers to learn this task. 17 untrained pathologists first classified 8 WSIs and 200 image patches (highly relevant for algorithmic classification) of ccMCTs as either positive or negative for c-KIT-11-ITD. Second, they self-trained to recognize c-KIT-11-ITD by looking at the same WSIs and patches correctly sorted. Third, pathologists classified 15 new WSIs and 200 new patches according to c-KIT-11-ITD status. In addition, participants reported microscopic features they considered relevant for their decision. Without training, participants correctly classified the c-KIT-11-ITD status of 63%–88% of WSIs and 43%–55% of patches. With self-training, 25%–38% of WSIs and 55%–56% of patches were correctly classified. High cellular pleomorphism, anisokaryosis, and sparse cytoplasmic granulation were commonly suggested as features associated with c-KIT-11-ITD-positive ccMCTs, none of which showed reliable predictivity in a follow-up study. The results indicate that transfer of algorithmic skills to the human observer is difficult. A c-KIT-11-ITD-specific morphological feature remains to be extracted from the artificial intelligence model. UR - https://doi.org/10.1177/03009858251380284 Y1 - 2025 UR - https://doi.org/10.1177/03009858251380284 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63841 SN - 1544-2217 VL - 63 IS - 2 SP - 369 EP - 379 PB - Sage CY - London ER - TY - JOUR A1 - Rosbach, Emely A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Bertram, Christof A1 - Conrad, Thomas A1 - Riener, Andreas A1 - Aubreville, Marc T1 - Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology JF - Machine Learning for Biomedical Imaging N2 - Artificial intelligence (AI)-driven clinical decision support systems (CDSS) hold promise to improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts and AI may give rise to cognitive biases, such as automation and anchoring bias, wherein users may be inclined to blindly adopt system recommendations or be disproportionately influenced by the presence of AI predictions, even when they are inaccurate. These biases may be exacerbated under time pressure, pervasive in routine pathology diagnostics, or shaped by individual user characteristics. To investigate these effects, we conducted a web-based experiment in which trained pathology experts (n = 28) estimated tumor cell percentages twice: once independently and once with the aid of an AI. A subset of the estimates in each condition was performed under time constraints. Our findings indicate that AI integration generally enhances diagnostic performance. However, it also introduced a 7% automation bias rate, quantified as the number of accepted negative consultations, where a previously correct independent assessment gets overturned by inaccurate AI guidance. While time pressure did not increase the frequency of automation bias occurrence, it appeared to intensify its severity, as evidenced by a performance decline linked to increased automation reliance under cognitive load. A linear mixed-effects model (LMM) analysis, simulating weighted averaging, revealed a statistically significant positive coefficient for AI advice, indicating a moderate degree of anchoring on system output. This effect was further intensified under time pressure, suggesting that anchoring bias may become more pronounced when cognitive resources are limited. A secondary LMM evaluation assessing automation reliance, used as a proxy for both automation and anchoring bias, demonstrated that professional experience and self-efficacy were associated with reduced dependence on system support, whereas higher confidence during AI-assisted decision-making was linked to increased automation reliance. Together, these findings underscore the dual nature of AI integration in clinical workflows, offering performance benefits while also introducing risks of cognitive bias–driven diagnostic errors. As an initial investigation focused on a single medical specialty and diagnostic task, this study aims to lay the groundwork for future research to explore these phenomena across diverse clinical contexts, ultimately supporting the establishment of appropriate reliance on automated systems and the safe, effective integration of human–AI collaboration in medical decision-making. UR - https://doi.org/10.59275/j.melba.2026-87b1 Y1 - 2026 UR - https://doi.org/10.59275/j.melba.2026-87b1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-67787 SN - 2766-905X VL - 3 IS - MELBA–BVM 2025 Special Issue SP - 126 EP - 147 PB - Melba editors CY - [s. l.] ER - TY - JOUR A1 - Puget, Chloé A1 - Ganz, Jonathan A1 - Ostermaier, Julian A1 - Conrad, Thomas A1 - Parlak, Eda A1 - Bertram, Christof A1 - Kiupel, Matti A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Klopfleisch, Robert T1 - Artificial intelligence can be trained to predict c-KIT-11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides JF - Veterinary Pathology N2 - Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the c-KIT gene ( c-KIT-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with c-KIT-11-ITD solely based on morphology. Hematoxylin and eosin (HE) stained slides of 368 cutaneous, subcutaneous, and mucocutaneous MCTs (195 with ITD and 173 without) were stained consecutively in 2 different laboratories and scanned with 3 different slide scanners. This resulted in 6 data sets (stain-scanner variations representing diagnostic institutions) of whole-slide images. DLMs were trained with single and mixed data sets and their performances were assessed under stain-scanner variations (domain shifts). The DLM correctly classified HE slides according to their c-KIT-11-ITD status in up to 87% of cases with a 0.90 sensitivity and a 0.83 specificity. A relevant performance drop could be observed when the stain-scanner combination of training and test data set differed. Multi-institutional data sets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant (ie, intra-institutional). In summary, DLM-based morphological examination can predict c-KIT-11-ITD with high accuracy in canine MCTs in HE slides. However, staining protocol and scanner type influence accuracy. Larger data sets of scans from different laboratories and scanners may lead to more robust DLMs to identify c- KIT mutations in HE slides. UR - https://doi.org/10.1177/03009858241286806 Y1 - 2024 UR - https://doi.org/10.1177/03009858241286806 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53323 SN - 1544-2217 SN - 0300-9858 VL - 62 IS - 2 SP - 152 EP - 160 PB - Sage CY - London ER - TY - JOUR A1 - Wilm, Frauke A1 - Fragoso-Garcia, Marco A1 - Marzahl, Christian A1 - Qiu, Jingna A1 - Puget, Chloé A1 - Diehl, Laura A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset JF - Scientific Data N2 - Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application. UR - https://doi.org/10.1038/s41597-022-01692-w Y1 - 2022 UR - https://doi.org/10.1038/s41597-022-01692-w UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28741 SN - 2052-4463 VL - 9 PB - Springer CY - London ER - TY - JOUR A1 - Aubreville, Marc A1 - Wilm, Frauke A1 - Stathonikos, Nikolas A1 - Breininger, Katharina A1 - Donovan, Taryn A1 - Jabari, Samir A1 - Veta, Mitko A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - van Diest, Paul J A1 - Klopfleisch, Robert A1 - Bertram, Christof T1 - A comprehensive multi-domain dataset for mitotic figure detection JF - Scientific Data N2 - The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species. UR - https://doi.org/10.1038/s41597-023-02327-4 Y1 - 2023 UR - https://doi.org/10.1038/s41597-023-02327-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40068 SN - 2052-4463 N1 - Author Correction verfügbar unter https://doi.org/10.1038/s41597-024-03548-x VL - 10 PB - Springer CY - London ER - TY - JOUR A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Donovan, Taryn A1 - Marzahl, Christian A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research JF - Scientific data N2 - Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset. UR - https://doi.org/10.1038/s41597-020-00756-z Y1 - 2020 UR - https://doi.org/10.1038/s41597-020-00756-z UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11765 SN - 2052-4463 VL - 7 PB - Springer CY - London ER - TY - INPR A1 - Ivan, Zsanett Zsofia A1 - Hirling, Dominik A1 - Grexa, Istvan A1 - Ammeling, Jonas A1 - Micsik, Tamas A1 - Dobra, Katalin A1 - Kuthi, Levente A1 - Sukosd, Farkas A1 - Aubreville, Marc A1 - Miczan, Vivien A1 - Horvath, Peter T1 - Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis N2 - Mitosis detection represents a critical task in the field of digital pathology, as determination of the mitotic index (MI) plays an important role in the tumor grading and prognostic assessment of patients. Manual determination of MI is a labor-intensive and time-consuming task for practitioners with rather high interobserver variability, thus, automation has become a priority. There has been substantial progress towards creating robust mitosis detection algorithms in recent years, primarily driven by the Mitosis Domain Generalization (MIDOG) challenges. In parallel, there has been growing interest in the molecular characterization of mitosis with the goal of achieving a more comprehensive understanding of its underlying mechanisms in a subphase-specific manner. Here, we introduce a new mitotic figure dataset annotated with subphase information based on the MIDOG++ dataset as well as a previously unrepresented tumor domain to enhance the diversity and applicability of the dataset. We envision a new perspective for domain generalization by improving the performance of models with subtyping mitotic cells into the 5 main stages of normal mitosis, complemented with an atypical mitotic class. We believe that our work broadens the horizon in digital pathology: subtyping information could provide useful help for mitosis detection, while also providing promising new directions in answering biological questions, such as molecular analysis of the subphases on a single cell level. UR - https://doi.org/10.1101/2025.07.17.665280 Y1 - 2025 UR - https://doi.org/10.1101/2025.07.17.665280 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64587 PB - bioRxiv CY - Cold Spring Harbor ER - TY - JOUR A1 - Marzahl, Christian A1 - Hill, Jenny A1 - Stayt, Jason A1 - Bienzle, Dorothee A1 - Welker, Lutz A1 - Wilm, Frauke A1 - Voigt, Jörn A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Breininger, Katharina A1 - Bertram, Christof T1 - Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens JF - Scientific Data N2 - Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered. UR - https://doi.org/10.1038/s41597-022-01389-0 Y1 - 2022 UR - https://doi.org/10.1038/s41597-022-01389-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29319 SN - 2052-4463 VL - 9 PB - Springer CY - London ER - TY - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Marzahl, Christian A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor JF - Scientific data N2 - We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms. UR - https://doi.org/10.1038/s41597-019-0290-4 Y1 - 2019 UR - https://doi.org/10.1038/s41597-019-0290-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11838 SN - 2052-4463 VL - 6 PB - Springer CY - London ER - TY - JOUR A1 - Ivan, Zsanett Zsofia A1 - Hirling, Dominik A1 - Grexa, Istvan A1 - Ammeling, Jonas A1 - Molnar, Csaba A1 - Micsik, Tamas A1 - Dobra, Katalin A1 - Kuthi, Levente A1 - Sukosd, Farkas A1 - Fillinger, Janos A1 - Moldvay, Judit A1 - Toth, Erika A1 - Aubreville, Marc A1 - Miczan, Vivien A1 - Horvath, Peter T1 - A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis JF - Scientific Data N2 - Mitosis detection represents a critical task in digital pathology, as it plays an important role in the tumor grading and prognosis of patients. Manual determination is a labor-intensive task for practitioners with high interobserver variability, thus, automation is a priority. There has been substantial progress towards creating robust mitosis detection algorithms, primarily driven by the Mitosis Domain Generalization (MIDOG) challenges. Also, there has been growing interest in the molecular characterization of mitosis to achieve a more comprehensive understanding of its underlying mechanisms in a subphase-specific manner. We introduce a new mitotic figure dataset annotated with subphase information based on the MIDOG++ dataset as well as a previously unrepresented tumor domain to enhance the diversity and applicability. We envision a new perspective for domain generalization by improving model performance with subtyping mitosis, complemented with an atypical mitotic class. Our work has implications in two main areas: subtyping information can provide helpful information in mitosis detection, while also providing promising new directions in answering biological questions, such as molecular analysis of subphases. UR - https://doi.org/10.1038/s41597-026-07007-7 Y1 - 2026 UR - https://doi.org/10.1038/s41597-026-07007-7 SN - 2052-4463 SP - 17 PB - Springer CY - London ER - TY - CHAP A1 - Bertram, Christof A1 - Weiss, Viktoria A1 - Donovan, Taryn A1 - Banerjee, Sweta A1 - Conrad, Thomas A1 - Ammeling, Jonas A1 - Klopfleisch, Robert A1 - Kaltenecker, Christopher A1 - Aubreville, Marc ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br) T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09–11, 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_25 Y1 - 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_25 SN - 978-3-658-47422-5 SP - 113 EP - 118 PB - Springer Vieweg CY - Wiesbaden ER - TY - INPR A1 - Aubreville, Marc A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Kaltenecker, Christopher A1 - Bertram, Christof T1 - Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis N2 - The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources. While it provides a huge data variety, the image quality and composition is highly heterogeneous, impacting its utility for text-conditional image synthesis. We propose an automatic pipeline that provides predictions of the most common impurities within the images, e.g., visibility of narrators, desktop environment and pathology software, or text within the image. Additionally, we propose to use semantic alignment filtering of the image-text pairs. Our findings demonstrate that by rigorously filtering the dataset, there is a substantial enhancement of image fidelity in text-to-image tasks. UR - https://doi.org/10.48550/arXiv.2404.07676 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2404.07676 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48648 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Banerjee, Sweta A1 - Gosch, Timo A1 - Hester, Sara A1 - Weiss, Viktoria A1 - Conrad, Thomas A1 - Donovan, Taryn A1 - Porsche, Nils A1 - Ammeling, Jonas A1 - Stroblberger, Christoph A1 - Klopfleisch, Robert A1 - Kaltenecker, Christopher A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Handels, Heinz ED - Breininger, Katharina ED - Deserno, Thomas Martin ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN T2 - Bildverarbeitung für die Medizin 2026: Proceedings, German Conference on Medical Image Computing, Lübeck, March 15–17, 2026 UR - https://doi.org/10.1007/978-3-658-51100-5_42 Y1 - 2026 UR - https://doi.org/10.1007/978-3-658-51100-5_42 SN - 978-3-658-51100-5 SP - 203 EP - 209 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Banerjee, Sweta A1 - Bertram, Christof A1 - Ammeling, Jonas A1 - Weiss, Viktoria A1 - Conrad, Thomas A1 - Klopfleisch, Robert A1 - Kaltenecker, Christopher A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09–11, 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_56 Y1 - 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_56 SN - 978-3-658-47422-5 SP - 260 EP - 265 PB - Springer Vieweg CY - Wiesbaden ER - TY - INPR A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Petrick, Anne A1 - Weiler, Kristina A1 - Gläsel, Agnes C. A1 - Fragoso-Garcia, Marco A1 - Merz, Sophie A1 - Bartenschlager, Florian A1 - Hoppe, Judith A1 - Langenhagen, Alina A1 - Jasensky, Anne-Katherine A1 - Voigt, Jörn A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides UR - https://doi.org/10.48550/arXiv.2004.05838 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2004.05838 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Bertram, Christof A1 - Veta, Mitko A1 - Marzahl, Christian A1 - Stathonikos, Nikolas A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Aubreville, Marc T1 - Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels UR - https://doi.org/10.48550/arXiv.2007.05351 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2007.05351 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Aubreville, Marc A1 - Ehrensperger, Kai A1 - Maier, Andreas A1 - Rosenkranz, Tobias A1 - Graf, Benjamin A1 - Puder, Henning T1 - Deep denoising for hearing aid applications UR - https://doi.org/10.48550/arXiv.1805.01198 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1805.01198 PB - arXiv CY - Ithaca ER -