@article{HaghoferFuchsBaumgartingerLipniketal.2023, author = {Haghofer, Andreas and Fuchs-Baumgartinger, Andrea and Lipnik, Karoline and Klopfleisch, Robert and Aubreville, Marc and Scharinger, Josef and Weissenb{\"o}ck, Herbert and Winkler, Stephan M. and Bertram, Christof}, title = {Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing}, volume = {13}, pages = {19436}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-023-46607-w}, year = {2023}, abstract = {AbstractHistopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92\% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84\% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21\%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users' trust in computer-assisted image classification.}, language = {en} } @article{HirlingTasnadiCaicedoetal.2023, author = {Hirling, Dominik and Tasnadi, Ervin and Caicedo, Juan and Caroprese, Maria V. and Sj{\"o}gren, Rickard and Aubreville, Marc and Koos, Krisztian and Horvath, Peter}, title = {Segmentation metric misinterpretations in bioimage analysis}, volume = {21}, journal = {Nature Methods}, number = {2}, publisher = {Springer Nature}, address = {Berlin}, issn = {1548-7091}, doi = {https://doi.org/10.1038/s41592-023-01942-8}, pages = {213 -- 216}, year = {2023}, abstract = {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.}, language = {en} } @article{SievertMantsopoulosMuelleretal.2022, author = {Sievert, Matti and Mantsopoulos, Konstantinos and M{\"u}ller, Sarina K. and Eckstein, Markus and Rupp, Robin and Aubreville, Marc and Stelzle, Florian and Oetter, Nicolai and Maier, Andreas and Iro, Heinrich and Goncalves, Miguel}, title = {Systematic interpretation of confocal laser endomicroscopy: larynx and pharynx confocal imaging score}, volume = {42}, journal = {Acta otorhinolaryngologica italica}, number = {1}, publisher = {Pacini}, address = {Pisa}, issn = {1827-675X}, doi = {https://doi.org/10.14639/0392-100X-N1643}, pages = {26 -- 33}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{QiuWilmOettletal.2023, author = {Qiu, Jingna and Wilm, Frauke and {\"O}ttl, Mathias and Schlereth, Maja and Liu, Chang and Heimann, Tobias and Aubreville, Marc and Breininger, Katharina}, title = {Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation}, booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2023: Proceedings, Part II}, editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-43895-0}, issn = {1611-3349}, doi = {https://doi.org/10.1007/978-3-031-43895-0_9}, pages = {90 -- 100}, year = {2023}, language = {en} } @inproceedings{GanzLipnikAmmelingetal.2023, author = {Ganz, Jonathan and Lipnik, Karoline and Ammeling, Jonas and Richter, Barbara and Puget, Chlo{\´e} and Parlak, Eda and Diehl, Laura and Klopfleisch, Robert and Donovan, Taryn and Kiupel, Matti and Bertram, Christof and Breininger, Katharina and Aubreville, Marc}, title = {Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, Braunschweig, July 2-4, 2023}, editor = {Deserno, Thomas Martin and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-41657-7}, doi = {https://doi.org/10.1007/978-3-658-41657-7_49}, pages = {226 -- 231}, year = {2023}, language = {en} } @inproceedings{WilmFragosoGarciaBertrametal.2023, author = {Wilm, Frauke and Fragoso-Garcia, Marco and Bertram, Christof and Stathonikos, Nikolas and {\"O}ttl, Mathias and Qiu, Jingna and Klopfleisch, Robert and Maier, Andreas and Aubreville, Marc and Breininger, Katharina}, title = {Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology}, booktitle = {2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7358-3}, doi = {https://doi.org/10.1109/ISBI53787.2023.10230458}, year = {2023}, language = {en} } @article{AubrevilleStathonikosDonovanetal.2024, author = {Aubreville, Marc and Stathonikos, Nikolas and Donovan, Taryn and Klopfleisch, Robert and Ammeling, Jonas and Ganz, Jonathan and Wilm, Frauke and Veta, Mitko and Jabari, Samir and Eckstein, Markus and Annuscheit, Jonas and Krumnow, Christian and Bozaba, Engin and Cayir, Sercan and Gu, Hongyan and Chen, Xiang and Jahanifar, Mostafa and Shephard, Adam and Kondo, Satoshi and Kasai, Satoshi and Kotte, Sujatha and Saipradeep, Vangala and Lafarge, Maxime W. and Koelzer, Viktor H. and Wang, Ziyue and Zhang, Yongbing and Yang, Sen and Wang, Xiyue and Breininger, Katharina and Bertram, Christof}, title = {Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge}, volume = {2024}, pages = {103155}, journal = {Medical Image Analysis}, number = {94}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1361-8423}, doi = {https://doi.org/10.1016/j.media.2024.103155}, year = {2024}, abstract = {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.}, language = {en} } @article{GanzMarzahlAmmelingetal.2024, author = {Ganz, Jonathan and Marzahl, Christian and Ammeling, Jonas and Rosbach, Emely and Richter, Barbara and Puget, Chlo{\´e} and Denk, Daniela and Demeter, Elena A. and Tabaran, Flaviu A. and Wasinger, Gabriel and Lipnik, Karoline and Tecilla, Marco and Valentine, Matthew J. and Dark, Michael and Abele, Niklas and Bolfa, Pompei and Erber, Ramona and Klopfleisch, Robert and Merz, Sophie and Donovan, Taryn and Jabari, Samir and Bertram, Christof and Breininger, Katharina and Aubreville, Marc}, title = {Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H\&E slide analysis}, volume = {14}, pages = {26273}, journal = {Scientific Reports}, number = {1}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-024-77244-6}, year = {2024}, abstract = {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.}, language = {en} } @article{HaghoferParlakBarteletal.2024, author = {Haghofer, Andreas and Parlak, Eda and Bartel, Alexander and Donovan, Taryn and Assenmacher, Charles-Antoine and Bolfa, Pompei and Dark, Michael and Fuchs-Baumgartinger, Andrea and Klang, Andrea and J{\"a}ger, Kathrin and Klopfleisch, Robert and Merz, Sophie and Richter, Barbara and Schulman, F. Yvonne and Janout, Hannah and Ganz, Jonathan and Scharinger, Josef and Aubreville, Marc and Winkler, Stephan M. and Kiupel, Matti and Bertram, Christof}, title = {Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry}, volume = {62}, journal = {Veterinary Pathology}, number = {2}, publisher = {Sage}, address = {London}, issn = {1544-2217}, doi = {https://doi.org/10.1177/03009858241295399}, pages = {161 -- 177}, year = {2024}, abstract = {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.}, language = {en} } @article{StathonikosAubrevilledeVriesetal.2024, author = {Stathonikos, Nikolas and Aubreville, Marc and de Vries, Sjoerd and Wilm, Frauke and Bertram, Christof and Veta, Mitko and van Diest, Paul J}, title = {Breast cancer survival prediction using an automated mitosis detection pipeline}, volume = {10}, pages = {e70008}, journal = {The Journal of Pathology: Clinical Research}, number = {6}, publisher = {Wiley}, address = {Chichester}, issn = {2056-4538}, doi = {https://doi.org/10.1002/2056-4538.70008}, year = {2024}, abstract = {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.}, language = {en} } @unpublished{QiuAubrevilleWilmetal.2024, author = {Qiu, Jingna and Aubreville, Marc and Wilm, Frauke and {\"O}ttl, Mathias and Utz, Jonas and Schlereth, Maja and Breininger, Katharina}, title = {Leveraging Image Captions for Selective Whole Slide Image Annotation}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2407.06363}, year = {2024}, language = {en} } @article{GlahnHaghoferDonovanetal.2024, author = {Glahn, Imaine and Haghofer, Andreas and Donovan, Taryn and Degasperi, Brigitte and Bartel, Alexander and Kreilmeier-Berger, Theresa and Hyndman, Philip S. and Janout, Hannah and Assenmacher, Charles-Antoine and Bartenschlager, Florian and Bolfa, Pompei and Dark, Michael and Klang, Andrea and Klopfleisch, Robert and Merz, Sophie and Richter, Barbara and Schulman, F. Yvonne and Ganz, Jonathan and Scharinger, Josef and Aubreville, Marc and Winkler, Stephan M. and Bertram, Christof}, title = {Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility}, volume = {11}, pages = {278}, journal = {Veterinary Sciences}, number = {6}, publisher = {MDPI}, address = {Basel}, issn = {2306-7381}, doi = {https://doi.org/10.3390/vetsci11060278}, year = {2024}, abstract = {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.}, language = {en} } @article{GanzAmmelingJabarietal.2024, author = {Ganz, Jonathan and Ammeling, Jonas and Jabari, Samir and Breininger, Katharina and Aubreville, Marc}, title = {Re-identification from histopathology images}, volume = {2025}, pages = {103335}, journal = {Medical Image Analysis}, number = {99}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1361-8423}, doi = {https://doi.org/10.1016/j.media.2024.103335}, year = {2024}, abstract = {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.}, language = {en} } @article{AubrevilleGanzAmmelingetal.2024, author = {Aubreville, Marc and Ganz, Jonathan and Ammeling, Jonas and Rosbach, Emely and Gehrke, Thomas and Scherzad, Agmal and Hackenberg, Stephan and Goncalves, Miguel}, title = {Prediction of tumor board procedural recommendations using large language models}, volume = {282}, journal = {European Archives of Oto-Rhino-Laryngology}, number = {3}, publisher = {Springer}, address = {Berlin}, issn = {1434-4726}, doi = {https://doi.org/10.1007/s00405-024-08947-9}, pages = {1619 -- 1629}, year = {2024}, language = {en} } @article{FrenkenSievertPanugantietal.2024, author = {Frenken, Ann-Kathrin and Sievert, Matti and Panuganti, Bharat and Aubreville, Marc and Meyer, Till and Scherzad, Agmal and Gehrke, Thomas and Scheich, Matthias and Hackenberg, Stephan and Goncalves, Miguel}, title = {Feasibility of Optical Biopsy During Endoscopic Sinus Surgery With Confocal Laser Endomicroscopy: A Pilot Study}, volume = {134}, journal = {The Laryngoscope}, number = {10}, publisher = {Wiley}, address = {Malden}, issn = {1531-4995}, doi = {https://doi.org/10.1002/lary.31503}, pages = {4217 -- 4224}, year = {2024}, language = {en} } @inproceedings{AmmelingHeckerGanzetal.2024, author = {Ammeling, Jonas and Hecker, Moritz and Ganz, Jonathan and Donovan, Taryn and Klopfleisch, Robert and Bertram, Christof and Breininger, Katharina and Aubreville, Marc}, title = {Automated Mitotic Index Calculation via Deep Learning and Immunohistochemistry}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2024: Proceedings, German Conference on Medical Image Computing, Erlangen, March 10-12, 2024}, editor = {Maier, Andreas and Deserno, Thomas Martin and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-44037-4}, doi = {https://doi.org/10.1007/978-3-658-44037-4_37}, pages = {123 -- 128}, year = {2024}, language = {en} } @article{SievertAubrevilleMuelleretal.2024, author = {Sievert, Matti and Aubreville, Marc and Mueller, Sarina Katrin and Eckstein, Markus and Breininger, Katharina and Iro, Heinrich and Goncalves, Miguel}, title = {Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision}, volume = {281}, journal = {European Archives of Oto-Rhino-Laryngology}, number = {4}, publisher = {Springer}, address = {Berlin}, issn = {1434-4726}, doi = {https://doi.org/10.1007/s00405-024-08476-5}, pages = {2115 -- 2122}, year = {2024}, language = {en} } @article{Aubreville2024, author = {Aubreville, Marc}, title = {Developing Robust AI Applications for Clinical Use: The Special Case of Pathology}, volume = {3}, journal = {Trillium Pathology}, number = {1}, publisher = {Trillium GmbH Medizinischer Fachverlag}, address = {Grafrath}, issn = {2752-1915}, doi = {https://doi.org/10.47184/tp.2024.01.04}, pages = {20 -- 22}, year = {2024}, language = {en} } @inproceedings{PerniasRampasRichteretal.2024, author = {Pernias, Pablo and Rampas, Dominic and Richter, Mats Leon and Pal, Christopher and Aubreville, Marc}, title = {W{\"u}rstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models}, booktitle = {The Twelfth International Conference on Learning Representations (ICLR 2024)}, publisher = {OpenReview}, url = {https://openreview.net/forum?id=gU58d5QeGv}, year = {2024}, language = {en} } @inproceedings{EisenmannReinkeWeruetal.2023, author = {Eisenmann, Matthias and Reinke, Annika and Weru, Vivienn and Tizabi, Minu Dietlinde and Isensee, Fabian and Adler, Tim J. and Ali, Sharib and Andrearczyk, Vincent and Aubreville, Marc and Baid, Ujjwal and Bakas, Spyridon and Balu, Niranjan and Bano, Sophia and Bernal, Jorge and Bodenstedt, Sebastian and Casella, Alessandro and Cheplygina, Veronika and Daum, Marie and De Bruijne, Marleen and Depeursinge, Adrien and Dorent, Reuben and Egger, Jan and Ellis, David G. and Engelhardt, Sandy and Ganz, Melanie and Ghatwary, Noha M. and Girard, Gabriel and Godau, Patrick and Gupta, Anubha and Hansen, Lasse and Harada, Kanako and Heinrich, Mattias and Heller, Nicholas and Hering, Alessa and Huaulm{\´e}, Arnoud and Jannin, Pierre and Kavur, A. Emre and Kodym, Oldrich and Kozubek, Michal and Li, Jianning and Li, Hongwei and Ma, Jun and Mart{\´i}n-Isla, Carlos and Menze, Bjoern H. and Noble, Alison and Oreiller, Valentin and Padoy, Nicolas and Pati, Sarthak and Payette, Kelly and R{\"a}dsch, Tim and Rafael-Pati{\~n}o, Jonathan and Bawa, Vivek Singh and Speidel, Stefanie and Sudre, Carole H. and Van Wijnen, Kimberlin M. H. and Wagner, M. and Wei, D. and Yamlahi, Amine and Yap, Moi Hoon and Yuan, C. and Zenk, Maximilian and Zia, A. and Zimmerer, David and Aydogan, Dogu Baran and Bhattarai, B. and Bloch, Louise and Br{\"u}ngel, Raphael and Cho, J. and Choi, C. and Dou, Q. and Ezhov, Ivan and Friedrich, Christoph M. and Fuller, C. and Gaire, Rebati Raman and Galdran, Adrian and Garc{\´i}a-Faura, {\´A}lvaro and Grammatikopoulou, Maria and Hong, S. and Jahanifar, Mostafa and Jang, I. and Kadkhodamohammadi, Abdolrahim and Kang, I. and Kofler, Florian and Kondo, Satoshi and Kuijf, Hugo Jaco and Li, M. and Luu, M. and Martinčič, Tomaz and Morais, P. and Naser, M. A. and Oliveira, B. and Owen, D. and Pang, S. and Park, Jinah and Park, S. and Płotka, S. and Puybareau, {\´E}lodie and Rajpoot, Nasir M. and Ryu, K. and Saeed, N. and Shephard, Adam and Shi, P. and Štepec, Dejan and Subedi, Ronast and Tochon, Guillaume and Torres, Helena R. and Urien, H{\´e}l{\`e}ne and Vila{\c{c}}a, Jo{\~a}o L. and Wahid, Kareem A. and Wang, H. and Wang, J. and Wang, L. and Wang, Xiyue and Wiestler, Benedikt and Wodzinski, Marek and Xia, F. and Xie, J. and Xiong, Z. and Yang, Sen and Yang, Y. and Zhao, Z. and Maier-Hein, Klaus H. and J{\"a}ger, Paul F. and Kopp-Schneider, Annette and Maier-Hein, Lena}, title = {Why is the Winner the Best?}, booktitle = {Proceedings: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {979-8-3503-0129-8}, issn = {2575-7075}, doi = {https://doi.org/10.1109/CVPR52729.2023.01911}, pages = {19955 -- 19967}, year = {2023}, language = {en} }