Publikationen

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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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".

Abstract

"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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- 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
  
  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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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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- DOI Verweis
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Weiterführende Links
- 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
  
  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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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
- Suche auf Google Scholar
- DOI Verweis
Exportformate
Weiterführende Links
- 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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Prediction of tumor board procedural recommendations using large language models

Aubreville, M. ., Ganz, J. ., Ammeling, J. ., Rosbach, E. ., Gehrke, T. ., Scherzad, A. ., … Goncalves, M. . (2024). Prediction of tumor board procedural recommendations using large language models. European Archives of Oto-Rhino-Laryngology. http://doi.org/10.1007/s00405-024-08947-9 (Original work published 2025)

Abstract

Multidisciplinary tumor boards are meetings where a team of medical specialists, including medical oncologists, radiation oncologists, radiologists, surgeons, and pathologists, collaborate to determine the best treatment plan for cancer patients. While decision-making in this context is logistically and cost-intensive, it has a significant positive effect on overall cancer survival.
- Suche auf Google Scholar
- DOI Verweis
Exportformate
Weiterführende Links
- Personenseiten
  
  Aubreville, Marc
- Abteilung/Einrichtung
  
  FLAIR - Flensburg Artificial Intelligence Research
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Digitalisierung im Gesundheitswesen
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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Re-identification from histopathology images

Ganz, J. ., Ammeling, J. ., Jabari, S. ., Breininger, K. ., & Aubreville, M. . (2024). Re-identification from histopathology images. Medical Image Analysis, 103335. http://doi.org/10.1016/j.media.2024.103335 (Original work published 2025)
- Suche auf Google Scholar
- DOI Verweis
Exportformate
Weiterführende Links
- Personenseiten
  
  Aubreville, Marc
- Abteilung/Einrichtung
  
  FLAIR - Flensburg Artificial Intelligence Research
- Forschungsfeld
  
  Digitalisierung im Gesundheitswesen
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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Prediction of tumor board procedural recommendations using large language models

Aubreville, M. ., Ganz, J. ., Ammeling, J. ., Rosbach, E. ., Gehrke, T. ., Scherzad, A. ., … Goncalves, M. . (2024). Prediction of tumor board procedural recommendations using large language models. European Archives of Oto-Rhino-Laryngology. http://doi.org/10.1007/s00405-024-08947-9 (Original work published 2025)

Abstract

Multidisciplinary tumor boards are meetings where a team of medical specialists, including medical oncologists, radiation oncologists, radiologists, surgeons, and pathologists, collaborate to determine the best treatment plan for cancer patients. While decision-making in this context is logistically and cost-intensive, it has a significant positive effect on overall cancer survival.
- Suche auf Google Scholar
- DOI Verweis
Exportformate
Weiterführende Links
- Personenseiten
  
  Aubreville, Marc
- Abteilung/Einrichtung
  
  FLAIR - Flensburg Artificial Intelligence Research
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Digitalisierung im Gesundheitswesen
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
Filter Tags
Re-identification from histopathology images

Ganz, J. ., Ammeling, J. ., Jabari, S. ., Breininger, K. ., & Aubreville, M. . (2024). Re-identification from histopathology images. Medical Image Analysis, 103335. http://doi.org/10.1016/j.media.2024.103335 (Original work published 2025)
- Suche auf Google Scholar
- DOI Verweis
Exportformate
Weiterführende Links
- Personenseiten
  
  Aubreville, Marc
- Abteilung/Einrichtung
  
  FLAIR - Flensburg Artificial Intelligence Research
- Forschungsfeld
  
  Digitalisierung im Gesundheitswesen
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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QUIC(K) communication for GPU Virtualization in Edge Computing

Lübben, R. ., Gaikwad, N. B., Stefanović, Čedomir ., & Kosta, S. . (2024). QUIC(K) communication for GPU Virtualization in Edge Computing. In International Conference on Wireless and Mobile Computing, Networking and Communications. Paris: IEEE. (Original work published Oktober 2024)
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  Lübben, Ralf
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  FLAIR - Flensburg Artificial Intelligence Research
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  Data Science und Künstliche Intelligenz
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  Fachbereich 4: Wirtschaft
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QUIC(K) communication for GPU Virtualization in Edge Computing

Lübben, R. ., Gaikwad, N. B., Stefanović, Čedomir ., & Kosta, S. . (2024). QUIC(K) communication for GPU Virtualization in Edge Computing. In International Conference on Wireless and Mobile Computing, Networking and Communications. Paris: IEEE. (Original work published Oktober 2024)
- Suche auf Google Scholar
Exportformate
Weiterführende Links
- Personenseiten
  
  Lübben, Ralf
- Abteilung/Einrichtung
  
  FLAIR - Flensburg Artificial Intelligence Research
- Forschungsfeld
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 4: Wirtschaft
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