TY - CHAP 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 ED - Yamashita, Naomi ED - Evers, Vanessa ED - Yatani, Koji ED - Ding, Xianghua ED - Lee, Bongshin ED - Chetty, Marshini ED - Toups-Dugas, Phoebe T1 - "When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology T2 - CHI'25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems N2 - Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, like confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may increase under time pressure, a ubiquitous factor in routine pathology, as it strains practitioners’ cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration fuels confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI in healthcare and aim to support the safe integration of clinical decision support systems. UR - https://doi.org/10.1145/3706598.3713319 Y1 - 2025 UR - https://doi.org/10.1145/3706598.3713319 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58797 SN - 979-8-4007-1394-1 PB - ACM CY - New York ER - 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 - JOUR A1 - Ammeling, Jonas A1 - Aubreville, Marc A1 - Fritz, Alexis A1 - Kießig, Angelika A1 - Krügel, Sebastian A1 - Uhl, Matthias T1 - An interdisciplinary perspective on AI-supported decision making in medicine JF - Technology in Society N2 - Artificial intelligence (AI)-supported medical diagnosis offers the potential to utilize the collaborative intelligence of context-sensitive humans and narrowly focused machines for patients’ benefit. The employment of machine-learning-based decision-support systems (MLDSS) in medicine, however, raises important multidisciplinary challenges that cannot be addressed in isolation. We discuss three disciplinary perspectives on the topic and their interplay. Ethical issues arise at the level of changing responsibility structures in healthcare. Behavioral issues relate to the actual impact that the system has on physicians. Technical issues arise with respect to the training of a machine learning (ML) model that gives accurate advice. We argue that the interaction between physicians and MLDSS including the concrete design of the interface in which this interaction occurs can only be considered at the intersection of all three disciplines. UR - https://doi.org/10.1016/j.techsoc.2024.102791 Y1 - 2024 UR - https://doi.org/10.1016/j.techsoc.2024.102791 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-56526 SN - 1879-3274 VL - 2025 IS - 81 PB - Elsevier CY - Amsterdam 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 - JOUR A1 - Goncalves, Miguel A1 - Aubreville, Marc A1 - Müller, Sarina K. A1 - Sievert, Matti A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Bohr, Christopher T1 - Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds JF - ACTA Otorhinolaryngologica Italica N2 - Probe-based confocal laser endomicroscopy (CLE) is an innovative technique for real-time, non-invasive analysis of the surface epithelium. While being successfully used for diagnosis by experts, this method has not yet been established in clinical routine, partly due to the lack of standards and criteria for classifying various lesions. Our aim was to determine the diagnostic value and inter-rater reliability of CLE in detecting malignant lesions of the vocal cords. 58 video sequences were extracted from the probe-based CLE (GastroFlex probe with a Cellvizio® laser system) examinations of 3 patients with squamous cell carcinomas and 4 patients with benign alterations of the vocal folds. Two ENT surgeons, who were blinded to the histological result, were asked to identify the sequences representing a carcinoma. We showed an accuracy, sensitivity, specificity, PPV and NPV of 91.38-96.55%, 100%, 87.8-95.2%, 77.27-89.47% and 100%, respectively, with an inter-rater reliability of k = 0.89 (“almost perfect agreement”). Probe-based CLE is a promising method for diagnosis and assessment of vocal fold lesions in vivo. Our results suggest that, with adequate training, the diagnostic value of this technique can be improved and potentially provide important information during oncological surgery. UR - https://doi.org/10.14639/0392-100X-2121 KW - Confocal laser endomicroscopy KW - Laryngeal cancer KW - Non-invasive histological imaging KW - Vocal folds Y1 - 2019 UR - https://doi.org/10.14639/0392-100X-2121 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11824 SN - 1827-675X VL - 39 IS - 6 SP - 389 EP - 395 PB - Pacini CY - Pisa 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 - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Gurtner, Corinne A1 - Bartel, Alexander A1 - Corner, Sarah M. A1 - Dettwiler, Martina A1 - Kershaw, Olivia A1 - Noland, Erica L. A1 - Schmidt, Anja A1 - Sledge, Dodd G. A1 - Smedley, Rebecca C. A1 - Thaiwong, Tuddow A1 - Kiupel, Matti A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent JF - Veterinary Pathology UR - https://doi.org/10.1177/0300985819890686 KW - area selection KW - high-power field KW - mitotic activity KW - mitotic figure distribution KW - tumor grading KW - tumor periphery Y1 - 2020 UR - https://doi.org/10.1177/0300985819890686 SN - 1544-2217 VL - 57 IS - 2 SP - 214 EP - 226 PB - Sage CY - London ER -