Publikationen

2025

• REACH: A Card-based Approach to Guide Research Engagement and Science Communication Methods

  Amidi, M., Horstmann, A. C., Mertineit, J. A., & Wallbaum, T. (2025). REACH: A Card-based Approach to Guide Research Engagement and Science Communication Methods. In Proceedings of the 2025 Mensch und Computer 2025 (S. 694–699).

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    • Amidi, Maryam
    • Wallbaum, Torben

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  • 2025
  • Conference Paper
  • • Maryam Amidi
    • Aike Horstmann
    • Jana Mertineit
    • Torben Wallbaum

• 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

    • 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

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    • Informatik und Gestaltung

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

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    • 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

• 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

    • 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

• MeasureTip: Towards a Finger-Worn Measurement Device

  Olberding, S., & Wallbaum, T. (2025). MeasureTip: Towards a Finger-Worn Measurement Device. In Proceedings of the 2025 Mensch und Computer 2025 (S. 673–677).

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    • Wallbaum, Torben

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  • 2025
  • Conference Paper
  • • Simon Olberding
    • Torben Wallbaum

• 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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  • Personenseiten

    • Aubreville, Marc

  • Forschungsprojekt

    • 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