Publikationen

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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Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)

Bertram, C. A. ", Weiss, V., Donovan, T. A., Banerjee, S., Conrad, T., Ammeling, J., … Aubreville, M. (2025). Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br). In "Bildverarbeitung für die Medizin 2025" (S. 113–118). "Wiesbaden": "Springer Fachmedien Wiesbaden".

Abstract

"Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic criterion for breast cancer. AMFs are an indicator of mutations in the genes regulating the cell cycle and can lead to aberrant chromosome constitution (aneuploidy) of the tumor cells. To facilitate further research on this topic using pattern recognition, we present the first ever publicly available dataset of atypical and normal MFs (AMi-Br). For this, we utilized two of the most popular MF datasets (MIDOG 2021 and TUPAC) and subclassified all MFs using a three expert majority vote. Our final dataset consists of 3,720 MFs, split into 832 AMFs (22.4\%) and 2,888 normal MFs (77.6\%) across all 223 tumor cases in the combined set. We provide baseline classification experiments to investigate the consistency of the dataset, using a Monte Carlo cross-validation and different strategies to combat class imbalance.We found an averaged balanced accuracy of up to 0.806 when using a patch-level data set split, and up to 0.713 when using a patient-level split."
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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
  
  CIVU – Center for Interaction, Visualization and Usability
- Profilbereich
  
  Gesundheit und Life Sciences
- Forschungsfeld
  
  Digitale Transformation
  
  Human-Computer-Interaction
  
  Data Science und Künstliche Intelligenz
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  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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- 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
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  Fachbereich 3: Information und Kommunikation
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Effortless Vision-Language Model Specialization in Histopathology without Annotation

Qiu, J., Jain, N., Ammeling, J., Aubreville, M., & Breininger, K. (2025). Effortless Vision-Language Model Specialization in Histopathology without Annotation. In MICCAI Workshop on Computational Pathology with Multimodal Data (COMPAYL). Abgerufen von https://openreview.net/forum?id=pQDzXVeBja
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  Aubreville, Marc
- 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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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.
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- DOI Verweis
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- 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)
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- DOI Verweis
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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
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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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QUIC(K) communication for GPU Virtualization in Edge Computing

Lübben, R., Gaikwad, N. B., Stefanović, Č., & 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, Č., & 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
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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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