TY - CHAP A1 - Ganz, Jonathan A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Jabari, Samir A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Classification of visibility in multi-stain microscopy images T2 - Medical Imaging with Deep Learning: MIDL 2022 Short Papers KW - mitotic figure classification KW - digital pathology KW - deep learning KW - dual input networks KW - attention KW - immunohistochemical staining Y1 - 2022 UR - https://openreview.net/forum?id=-GsA-mUVmm 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 - 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 - 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 - 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 - 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 -