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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 -