Publikationen

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

  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

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability
    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

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    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Emely Rosbach
    • Jonas Ammeling
    • Sebastian Krügel
    • Angelika Kießig
    • Alexis Fritz
    • Jonathan Ganz
    • Chloé Puget
    • Taryn Donovan
    • Andrea Klang
    • Maximilian Köller
    • Pompei Bolfa
    • Marco Tecilla
    • Daniela Denk
    • Matti Kiupel
    • Georgios Paraschou
    • Mun Kok
    • Alexander Haake
    • Ronald De Krijger
    • Andreas Sonnen
    • Tanit Kasantikul
    • Gerry Dorrestein
    • Rebecca Smedley
    • Nikolas Stathonikos
    • Matthias Uhl
    • Christof Bertram
    • Andreas Riener
    • Marc Aubreville

• 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)

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  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

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability
    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Proceedings
  • • Emely Rosbach
    • Jonathan Ganz
    • Jonas Ammeling
    • Andreas Riener
    • Marc Aubreville

• 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

  • Forschungsprojekt

    • (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
    • CIVU – Center for Interaction, Visualization and Usability

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Journal Article
  • • Christof Bertram
    • Melissa Schutten
    • Lorenzo Ressel
    • Katharina Breininger
    • Joshua Webster
    • Marc Aubreville

• GPU Virtualization and Remoting Service for AI Acceleration at the Edge

  Gaikwad, N., Lübben, R., & Kosta, S. (2025). GPU Virtualization and Remoting Service for AI Acceleration at the Edge. In Summer School on Cyber-Physical Systems and Internet-of-Things. (Original work published Juli 2025)

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  • Forschungsprojekt

    • KI-Anwendungszentrum (KIAZ)

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 4: Wirtschaft

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  • 2025
  • Conference Paper
  • • Nikhil Gaikwad
    • Ralf Lübben
    • Sokol Kosta

• On the road to interactive LLM-based systematic mapping studies

  Petersen, K., & Gerken, J. M. (2025). On the road to interactive LLM-based systematic mapping studies. Information and Software Technology, 178. (Original work published Februar 2025)

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    • Petersen, Kai
    • Gerken, Jan Michael

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

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    • Fachbereich 4: Wirtschaft

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  • 2025
  • Journal Article
  • • Kai Petersen
    • Jan Gerken
  • • Large language models
    • Systematic mapping studies
    • GPT

• Is Self-supervision Enough?

  Ganz, J. ", Ammeling, J., Rosbach, E., Lausser, L., Bertram, C. A., Breininger, K., & Aubreville, M. (2025). Is Self-supervision Enough? In "Bildverarbeitung für die Medizin 2025" (S. 63–68). "Wiesbaden": "Springer Fachmedien Wiesbaden".

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  "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."

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    • Aubreville, Marc

  • Forschungsprojekt

    • (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

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Jonathan Ganz
    • Jonas Ammeling
    • Emely Rosbach
    • Ludwig Lausser
    • Christof Bertram
    • Katharina Breininger
    • Marc Aubreville

• 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

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  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

  • Forschungsprojekt

    • (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

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Sweta Banerjee
    • Christof Bertram
    • Jonas Ammeling
    • Viktoria Weiss
    • Thomas Conrad
    • Robert Klopfleisch
    • Christopher Kaltenecker
    • Katharina Breininger
    • Marc Aubreville

• Characterization of irradiated mucosa using confocal laser endomicroscopy in the upper aerodigestive tract

  Thesing, L., Sievert, M., Panuganti, B., Aubreville, M., Meyer, T., Müller-Diesing, F., … Goncalves, M. (2025). Characterization of irradiated mucosa using confocal laser endomicroscopy in the upper aerodigestive tract. European Archives of Oto-Rhino-Laryngology, 282, 2507–2514.

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    • Aubreville, Marc

  • Forschungsprojekt

    • Optical Biopsy of Sinonasal Tumors using Confocal Laser Endomicroscopy: A Clinical and Deep Learning-based Assessment and Visualization

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Journal Article
  • • Lisa Thesing
    • Matti Sievert
    • Bharat Panuganti
    • Marc Aubreville
    • Till Meyer
    • Flurin Müller-Diesing
    • Agmal Scherzad
    • Stephan Hackenberg
    • Miguel Goncalves

• 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

  • Forschungsprojekt

    • (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

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Journal Article
  • • Jonas Ammeling
    • Jonathan Ganz
    • Frauke Wilm
    • Katharina Breininger
    • Marc Aubreville
  • • Feature extraction
    • Object detection
    • Measurement
    • Adaptation models
    • histopathology
    • Training
    • Complexity theory
    • YOLO
    • Location awareness
    • Data mining
    • object detection
    • latent space discrimination

• Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification

  Banerjee, S., Weiss, V., Conrad, T., Donovan, T. A., Ammeling, J., Fick, R., … Aubreville, M. (2025). Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification. In Proceedings of the MICCAI Workshop on Computational Pathology (Bd. 316, S. 266–277). PMLR.

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    • Banerjee, Sweta
    • Aubreville, Marc

  • Forschungsprojekt

    • (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

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Sweta Banerjee
    • Viktoria Weiss
    • Thomas Conrad
    • Taryn Donovan
    • Jonas Ammeling
    • Rutger Fick
    • Jonas Utz
    • Robert Klopfleisch
    • Christopher Kaltenecker
    • Christof Bertram
    • Katharina Breininger
    • Marc Aubreville