TY - INPR A1 - Aubreville, Marc A1 - Stathonikos, Nikolas A1 - Donovan, Taryn A1 - Klopfleisch, Robert A1 - Ganz, Jonathan A1 - Ammeling, Jonas 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 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 consensus 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 F1 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. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, but with only minor changes in the order of participants in the ranking. UR - https://doi.org/10.48550/arXiv.2309.15589 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2309.15589 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41514 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Donovan, Taryn A1 - Bartel, Alexander A1 - Wilm, Frauke A1 - Marzahl, Christian 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 - Haverkamp, Ann-Kathrin A1 - Heier, Annabelle A1 - Lehmbecker, Annika A1 - Merz, Sophie A1 - Noland, Erica L. A1 - Plog, Stephanie A1 - Schmidt, Anja A1 - Sebastian, Franziska A1 - Sledge, Dodd G. A1 - Smedley, Rebecca C. A1 - Tecilla, Marco A1 - Thaiwong, Tuddow A1 - Fuchs-Baumgartinger, Andrea A1 - Meuten, Donald J. A1 - Breininger, Katharina A1 - Kiupel, Matti A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy JF - Veterinary Pathology N2 - The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs. UR - https://doi.org/10.1177/03009858211067478 KW - canine cutaneous mast cell tumors KW - artificial intelligence KW - digital pathology KW - deep learning KW - mitotic figures KW - mitotic count KW - automated image analysis KW - computer assistance Y1 - 2021 UR - https://doi.org/10.1177/03009858211067478 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13141 SN - 1544-2217 VL - 59 IS - 2 SP - 211 EP - 226 PB - Sage CY - London ER - TY - CHAP A1 - Marzahl, Christian A1 - Wilm, Frauke A1 - Tharun, Lars A1 - Perner, Sven A1 - Kröger, Christine A1 - Voigt, Jörn A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Robust quad-tree based registration on whole slide images T2 - Proceedings of Machine Learning Research: Proceedings of COMPAY 2021 KW - Registration KW - Microscopy KW - Pathology Y1 - 2021 UR - https://proceedings.mlr.press/v156/marzahl21a.html IS - 156 SP - 181 EP - 190 PB - PMLR CY - [s. l.] ER - TY - INPR 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 UR - https://doi.org/10.48550/arXiv.2211.16141 KW - Histopathology KW - Domain Shift KW - Representation Learning KW - Barlow Twins Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2211.16141 PB - arXiv CY - Ithaca 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 -