TY - INPR A1 - Rosbach, Emely A1 - Ammeling, Jonas A1 - Krügel, Sebastian A1 - Kießig, Angelika A1 - Fritz, Alexis A1 - Ganz, Jonathan A1 - Puget, Chloé A1 - Donovan, Taryn A1 - Klang, Andrea A1 - Köller, Maximilian C. A1 - Bolfa, Pompei A1 - Tecilla, Marco A1 - Denk, Daniela A1 - Kiupel, Matti A1 - Paraschou, Georgios A1 - Kok, Mun Keong A1 - Haake, Alexander F. H. A1 - de Krijger, Ronald R. A1 - Sonnen, Andreas F.-P. A1 - Kasantikul, Tanit A1 - Dorrestein, Gerry M. A1 - Smedley, Rebecca C. A1 - Stathonikos, Nikolas A1 - Uhl, Matthias A1 - Bertram, Christof A1 - Riener, Andreas A1 - Aubreville, Marc T1 - "When TwoWrongs Don’t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology UR - https://doi.org/10.48550/arXiv.2411.01007 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2411.01007 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Rosbach, Emely A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Riener, Andreas A1 - Aubreville, Marc T1 - Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology UR - https://doi.org/10.48550/arXiv.2411.00998 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2411.00998 ER - 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 - INPR A1 - Haghofer, Andreas A1 - Parlak, Eda A1 - Bartel, Alexander A1 - Donovan, Taryn A1 - Assenmacher, Charles-Antoine A1 - Bolfa, Pompei A1 - Dark, Michael A1 - Fuchs-Baumgartinger, Andrea A1 - Klang, Andrea A1 - Jäger, Kathrin A1 - Klopfleisch, Robert A1 - Merz, Sophie A1 - Richter, Barbara A1 - Schulman, F. Yvonne A1 - Ganz, Jonathan A1 - Scharinger, Josef A1 - Aubreville, Marc A1 - Winkler, Stephan M. A1 - Kiupel, Matti A1 - Bertram, Christof T1 - Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors N2 - Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area ≥9.0μm2) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area ≥10.9μm2) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair (κ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements. UR - https://doi.org/10.48550/arXiv.2309.15031 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2309.15031 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41401 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Ganz, Jonathan A1 - Marzahl, Christian A1 - Ammeling, Jonas 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 - On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images 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. Deep learning algorithms can standardize this task, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithm's performance. Unlike H&E, 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 stain alone, the use of this ground truth could potentially introduce 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. We found that the annotators' object-level agreement increased when using PHH3-assisted labeling. Subsequently, MF detectors were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models' performance. Additionally, a novel dual-stain MF detector was developed to investigate the interpretation-shift of PHH3-assisted labels used in H&E, which clearly outperformed single-stain detectors. However, the PHH3-assisted labels did not have a positive effect on solely H&E-based models. The high performance of our dual-input detector reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect. UR - https://doi.org/10.48550/arXiv.2406.19899 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2406.19899 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-50155 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification N2 - Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key idea is to extract semantically rich vectors from any input patch, allowing for the use of simple subsequent classification networks potentially reducing the required amounts of labeled data, and increasing domain robustness. In this work, we investigate to which degree this also holds for mitotic figure classification. Utilizing two popular public mitotic figure datasets, we compared linear probing of five publicly available FMs against models trained on ImageNet and a simple ResNet50 end-to-end-trained baseline. We found that the end-to-end-trained baseline outperformed all FM-based classifiers, regardless of the amount of data provided. Additionally, we did not observe the FM-based classifiers to be more robust against domain shifts, rendering both of the above assumptions incorrect. UR - https://doi.org/10.48550/arXiv.2412.06365 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2412.06365 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59672 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Puget, Chloé A1 - Ganz, Jonathan A1 - Ostermaier, Julian A1 - Konrad, Thomas A1 - Parlak, Eda A1 - Bertram, Christof A1 - Kiupel, Matti A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Klopfleisch, Robert T1 - Deep Learning model predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumors by HE stained histological slides N2 - Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential success of a tyrosine kinase inhibitor therapy. This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were stained consecutively in two different laboratories and scanned with three different slide scanners. This resulted in six different datasets (stain-scanner variations) of whole slide images. DLMs were trained with single and mixed datasets and their performances was assessed under scanner and staining domain shifts. The DLMs correctly classified HE slides according to their c-Kit 11 mutation status in, on average, 87% of cases for the best-suited stain-scanner variant. A relevant performance drop could be observed when the stain-scanner combination of the training and test dataset differed. Multi-variant datasets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant. In summary, DLM-assisted morphological examination of MCTs can predict c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the recognition performance is impeded by a change of scanner or staining protocol. Larger data sets with higher numbers of scans originating from different laboratories and scanners may lead to more robust DLMs to identify c-Kit mutations in HE slides. UR - https://doi.org/10.48550/arXiv.2401.06169 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2401.06169 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46020 PB - arXiv CY - Ithaca ER - TY - INPR 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 T2 - Research Square 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/10.21203/rs.3.rs-4900505/v1 Y1 - 2024 UR - https://doi.org/10.21203/rs.3.rs-4900505/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57630 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - INPR A1 - Ammeling, Jonas A1 - Hecker, Moritz A1 - Ganz, Jonathan A1 - Donovan, Taryn A1 - Klopfleisch, Robert A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard N2 - The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not established as the standard method for assessing aggressive biological behaviour, due to the high additional workload associated with determining the epithelial proportion. In this work, we show that using a deep learning pipeline solely trained with an annotation-free, immunohistochemistry-based approach, provides accurate estimations of epithelial segmentation in canine breast carcinomas. We compare our automatic framework with the manually annotated M/V-Index in a study with three board-certified pathologists. Our results indicate that the deep learning-based pipeline shows expert-level performance, while providing time efficiency and reproducibility. UR - https://doi.org/10.48550/arXiv.2311.08949 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2311.08949 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41549 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Ammeling, Jonas A1 - Schmidt, Lars-Henning A1 - Ganz, Jonathan A1 - Niedermair, Tanja A1 - Brochhausen-Delius, Christoph A1 - Schulz, Christian A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays N2 - Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very small amounts of tissue from a TMA and reach similar C-index performance compared to established survival prediction methods trained with highly discriminative clinical factors such as age, cancer grade, and cancer stage. UR - https://doi.org/10.48550/arXiv.2212.07724 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2212.07724 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30642 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Aubreville, Marc A1 - Pan, Zhaoya A1 - Sievert, Matti A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Oetter, Nicolai A1 - Stelzle, Florian A1 - Frenken, Ann-Kathrin A1 - Breininger, Katharina A1 - Goncalves, Miguel T1 - Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors N2 - The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections. Confocal laser endomicroscopy (CLE) is an in-vivo imaging technique that has shown its potential in the live optical biopsy of tissue. An automated analysis of this notoriously difficult to interpret modality would help surgeons. However, the images of CLE show a wide variability of patterns, caused both by individual factors but also, and most strongly, by the anatomical structures of the imaged tissue, making it a challenging pattern recognition task. In this work, we evaluate four popular few shot learning (FSL) methods towards their capability of generalizing to unseen anatomical domains in CLE images. We evaluate this on images of sinunasal tumors (SNT) from five patients and on images of the vocal folds (VF) from 11 patients using a cross-validation scheme. The best respective approach reached a median accuracy of 79.6% on the rather homogeneous VF dataset, but only of 61.6% for the highly diverse SNT dataset. Our results indicate that FSL on CLE images is viable, but strongly affected by the number of patients, as well as the diversity of anatomical patterns. UR - https://doi.org/10.48550/arXiv.2311.07216 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2311.07216 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41522 PB - arXiv CY - Ithaca 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 - 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 -