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2025

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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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

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

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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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

2024

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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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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2024

Journal Article

Marc Aubreville

Jonathan Ganz

Jonas Ammeling

Emely Rosbach

Thomas Gehrke

Agmal Scherzad

Stephan Hackenberg

Miguel Goncalves
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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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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2024

Journal Article

Jonathan Ganz

Jonas Ammeling

Samir Jabari

Katharina Breininger

Marc Aubreville
Study on the impact of wind models and data processing on lidar-assisted control performance

Fu, W. ., Schlipf, D. ., Raach, S. ., White, S. ., Pitter, M. ., & Slinger, C. . (2024). Study on the impact of wind models and data processing on lidar-assisted control performance. http://doi.org/10.5281/zenodo.12666568 (Original work published 2025)
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Schlipf, David
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2024

Miscellaneous

Wei Fu

David Schlipf

Steffen Raach

Steven White

Mark Pitter

Chris Slinger
Contribution of wind energy education to resilience in the face of the climate crisis

Schlipf, D. ., Soto, G. Q., Rodriguez, H. Z., & Brovia, S. P. (2024). Contribution of wind energy education to resilience in the face of the climate crisis. http://doi.org/10.5281/zenodo.13270425 (Original work published 2025)
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Schlipf, David
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2024

Miscellaneous

David Schlipf

Grace Soto

Habib Rodriguez

Santiago Brovia
IEA Wind Task 52 LAC Summer Games 2024

Schlipf, D. ., Guo, F. ., Liang, Z. ., Pitter, M. ., Lopez-Queija, J. ., Rinker, J. ., … Weich, S. . (2024). IEA Wind Task 52 LAC Summer Games 2024. http://doi.org/10.5281/zenodo.13235189 (Original work published 2025)
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Schlipf, David
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2024

Miscellaneous

David Schlipf

Feng Guo

Zhi Liang

Mark Pitter

Javier Lopez-Queija

Jenni Rinker

Aravind Venkatachalapathy

Simon Weich
Subjective Quality Evaluation of 8-bit and 10-bit MP4-Coded Video Sequences from Netflix

Falkowski-Gilski, P. ., Uhl, T. ., & Divakarachari, P. . (2024). Subjective Quality Evaluation of 8-bit and 10-bit MP4-Coded Video Sequences from Netflix. Szczecin/Poland: Scientific Journals PMS. http://doi.org/10.17402/613 (Original work published Dezember 2024)
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Uhl, Tadeus

Forschungsfeld

IT-Security

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal

Przemyslaw Falkowski-Gilski

Tadeus Uhl

Parameshachari Divakarachari
Navigating the complexities of a changing world - Interdisciplinary Electives at Flensburg University of Applied Sciences

van der Sluis, H. ., & John, P. . (2024). Navigating the complexities of a changing world - Interdisciplinary Electives at Flensburg University of Applied Sciences. Educational Developments, 25.4, 4. Abgerufen von https://www.seda.ac.uk/seda-publishing/educational-developments/educational-developments-issue-25-4-2024/ (Original work published Dezember 2024)

Abstract

In a competitive Higher Education (HE) landscape, universities face the challenge of developing an organisational culture that supports the needs of its staff and students. The traditional staff development approach has been the attainment of skills through teaching and the completion of certificates of learning. This article challenges that practice and seeks to make the case for a renewed focus on the importance of learning through individual and group coaching practice to be ingrained into universities, shifting from a view of staff development purely as the acquisition of skills through training and teaching and moving towards
engaged learning.
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John, Peter

Abteilung/Einrichtung

Dekanat FB3

Lern- und Sprachenzentrum (LSZ)

Forschungsfeld

Fachkräfte und Vernetzung

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Hendrik van der Sluis

Peter John
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

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 4: Wirtschaft
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2024

Conference Paper

Ralf Lübben

Nikhil Gaikwad

Čedomir Stefanović

Sokol Kosta

Publikationen

2025

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2024

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