Publikationen

Exploring General-purpose Autonomous Multimodal Agents for Pathology Report Generation

Aubreville, M., Donovan, T. A., & Bertram, C. A. (2026). Exploring General-purpose Autonomous Multimodal Agents for Pathology Report Generation. In H. Handels, K. Breininger, T. Deserno, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Hrsg.), Bildverarbeitung für die Medizin 2026 (S. 393–399). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-51100-5_76
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  Aubreville, Marc
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  (mu)ROMI: Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence
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  FLAIR - Flensburg Artificial Intelligence Research
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  Data Science und Künstliche Intelligenz
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  Fachbereich 3: Information und Kommunikation
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Evaluation of ChatGPT Study Mode: Results from an Expert Rating Regarding Self-Regulated Learning

Steinberg, E., Perels, F., Aubreville, M., & Dörrenbächer-Ulrich, L. (2026). Evaluation of ChatGPT Study Mode: Results from an Expert Rating Regarding Self-Regulated Learning. Open Education Studies, 8, 20250169. http://doi.org/10.1515/edu-2025-0169
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  Aubreville, Marc
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  CIVU – Center for Interaction, Visualization and Usability
  
  FLAIR - Flensburg Artificial Intelligence Research
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  Fachbereich 3: Information und Kommunikation
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Data set creation for supervised deep learning–based analysis of microscopic images: Review of important considerations and recommendations

Bertram, C. A., Weiss, V., Ammeling, J., Schabel, M., Donovan, T. A., Wilm, F., … Aubreville, M. (2026). Data set creation for supervised deep learning–based analysis of microscopic images: Review of important considerations and recommendations. Veterinary Pathology, 03009858261457959. http://doi.org/10.1177/03009858261457959

Abstract

Supervised deep learning (DL) receives great interest for automated analysis of microscopic images with an increasing body of literature supporting its potential. The development and testing of those DL models rely heavily on the availability of high-quality, large-scale data sets. However, creating such data sets is a complex and resource-intensive process, often hindered by challenges such as time constraints, domain variability, and risks of bias in image collection and label creation. This review provides a comprehensive guide to the critical steps in data set creation, including (1) image acquisition, (2) selection of annotation software, and (3) annotation creation. For image acquisition, besides ensuring a sufficiently large number, it is important to address sources of image variability (domain shifts), such as those related to slide preparation and digitization, that could lead to algorithmic errors if not adequately represented in the training data. For annotations, key quality criteria are the 3 “C”s: correctness, consistency, and completeness. For mitigation of annotation bias of a single annotator, this review explores advanced annotation methods (eg, computer-assisted annotations). To support data set creators, a standard operating procedure is provided as supplemental material, summarizing all important considerations for data set creation. Furthermore, this article underscores the importance of open data sets in driving innovation and enhancing reproducibility of DL research. By addressing the challenges and offering practical recommendations, this review aims to advance the creation and availability of high-quality, large-scale data sets, ultimately contributing to the development of generalizable and robust DL models for pathology applications.
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Reporting Transparency in Veterinary Pathology Deep Learning: A Systematic Review of Reproducibility-Critical Details

Banerjee, S., Bertram, C. A., Weiss, V., Ammeling, J., Conrad, T., Porsche, N., … Aubreville, M. (2026). Reporting Transparency in Veterinary Pathology Deep Learning: A Systematic Review of Reproducibility-Critical Details. Veterinary Pathology. http://doi.org/10.1177/03009858261459452
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  Banerjee, Sweta
  
  Porsche, Nils
  
  Aubreville, Marc
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  (mu)ROMI: Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence
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  FLAIR - Flensburg Artificial Intelligence Research
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  Informatik und Gestaltung
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Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?

Puget, C., Ganz, J., Bertram, C. A., Conrad, T., Baeblich, M., Voss, A., … Klopfleisch, R. (2025). Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it? Veterinary Pathology, 03009858251380284. http://doi.org/10.1177/03009858251380284 (Original work published 2026)
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  Aubreville, Marc
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  FLAIR - Flensburg Artificial Intelligence Research
  
  CIVU – Center for Interaction, Visualization and Usability
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"When Two Wrongs Don t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology

Rosbach, E., Ammeling, J., Krügel, S., Kießig, A., Fritz, A., Ganz, J., … Aubreville, M. (2025). "When Two Wrongs Don t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (S. 1–18). Yokohama Japan: ACM. http://doi.org/10.1145/3706598.3713319 (Original work published 2026)
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  Aubreville, Marc
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  CIVU – Center for Interaction, Visualization and Usability
  
  FLAIR - Flensburg Artificial Intelligence Research
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  Gesundheit und Life Sciences
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Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology

Rosbach, E., Ganz, J., Ammeling, J., Riener, A., & Aubreville, M. (2025). Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology. Bildverarbeitung für die Medizin 2025. Wiesbaden: Springer Fachmedien Wiesbaden. (Original work published 2025)

Abstract

Artificial intelligence (AI)-based clinical decision support systems (CDSS) promise to enhance diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration might introduce automation bias, where users uncritically follow automated cues. This bias may worsen when time pressure strains practitioners’ cognitive resources. We quantified automation bias by measuring the adoption of negative system consultations and examined the role of time pressure in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results indicate that while AI integration led to a statistically significant increase in overall performance, it also resulted in a 7% automation bias rate, where initially correct evaluations were overturned by erroneous AI advice. Conversely, time pressure did not exacerbate automation bias occurrence, but appeared to increase its severity, evidenced by heightened reliance on the system’s negative consultations and subsequent performance decline. These findings highlight potential risks of AI use in healthcare. The final dataset (including a table with image patch details), participant demographics, and source code are available at: https://github.com/emelyrosbach/AB-TP.git
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  Aubreville, Marc
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  CIVU – Center for Interaction, Visualization and Usability
  
  FLAIR - Flensburg Artificial Intelligence Research
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Reporting guidelines for manuscripts that use artificial intelligence–based automated image analysis in Veterinary Pathology

Bertram, C. A., Schutten, M., Ressel, L., Breininger, K., Webster, J. D., & Aubreville, M. (2025). Reporting guidelines for manuscripts that use artificial intelligence–based automated image analysis in Veterinary Pathology. Veterinary Pathology, 62, 615–617. http://doi.org/10.1177/03009858251344320 (Original work published August 2025)
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  Aubreville, Marc
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  (mu)ROMI: Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence
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  FLAIR - Flensburg Artificial Intelligence Research
  
  CIVU – Center for Interaction, Visualization and Usability
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  Gesundheit und Life Sciences
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Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma

Banerjee, S., Bertram, C. A., Ammeling, J., Weiss, V., Conrad, T., Klopfleisch, R., … Aubreville, M. (2025). Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma. In Bildverarbeitung für die Medizin 2025 (S. 260–265). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-47422-5_56

Abstract

Automated tumor segmentation of histologic images is crucial for the development of computer-assisted diagnostic workflows aiming at accurate prognostication. We present a dataset of coarse annotations of over 1,000 highresolution breast tumor images from The cancer genome atlas breast invasive carcinoma (TCGA-BRCA) repository, each annotated with binary segmentation masks that delineate coarse tumor areas. Additionally, a subset of 20 images includes fine-grained annotations, providing precise delineation of tumor boundaries beyond the broader outlines used in coarse annotations. Initial evaluations using U-Net and DeepLabv3 models show promising segmentation performance. On a held-out, coarsely annotated test set, U-Net achieved an average intersection over union (IoU) score of 0.795, while DeepLabv3 scored 0.783. On the finely annotated subset of this test set, U-Net reached an average IoU of 0.746, with DeepLabv3 slightly outperforming at 0.765. The public availability of this dataset aims to support research in automated tumor analysis, advancing diagnostic workflows and thereby ultimately improving breast cancer management.
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  Banerjee, Sweta
  
  Aubreville, Marc
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  (mu)ROMI: Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence
- Abteilung/Einrichtung
  
  CIVU – Center for Interaction, Visualization and Usability
  
  FLAIR - Flensburg Artificial Intelligence Research
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  Gesundheit und Life Sciences
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  Data Science und Künstliche Intelligenz
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Investigation of Class Separability within Object Detection Models in Histopathology

Ammeling, J., Ganz, J., Wilm, F., Breininger, K., & Aubreville, M. (2025). Investigation of Class Separability within Object Detection Models in Histopathology. IEEE Transactions on Medical Imaging, 1–1. http://doi.org/10.1109/TMI.2025.3560134
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  Aubreville, Marc
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  (mu)ROMI: Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence
- Abteilung/Einrichtung
  
  FLAIR - Flensburg Artificial Intelligence Research
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Data Science und Künstliche Intelligenz
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  Fachbereich 3: Information und Kommunikation
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