Publikationen

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2025

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
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

Fachbereich

Fachbereich 3: Information und Kommunikation
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2025

Conference Paper

Jingna Qiu

Nishanth Jain

Jonas Ammeling

Marc Aubreville

Katharina Breininger
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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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

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Gesundheit und Life Sciences

Forschungsfeld

Digitale Transformation

Human-Computer-Interaction

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2025

Conference Paper

Christof Bertram

Viktoria Weiss

Taryn Donovan

Sweta Banerjee

Thomas Conrad

Jonas Ammeling

Robert Klopfleisch

Christopher Kaltenecker

Marc Aubreville
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

(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

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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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

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 2026)

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 2026)
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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
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

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
Dominance hierarchy does not influence distances travelled and area utilization in a large group of ponies

Weidling, G., Krieter, J., Lübben, R., & Czycholl, I. (2024). Dominance hierarchy does not influence distances travelled and area utilization in a large group of ponies. Applied Animal Behaviour Science, 271. http://doi.org/https://doi.org/10.1016/j.applanim.2024.106178 (Original work published Februar 2024)

Abstract

In order to improve existing horse husbandry practices by providing social contact and exercise opportunities, it is necessary to fully understand the behaviour of horses in relation to group housing. Therefore, this study investigated the influence of dominance hierarchy on the locomotor behaviour and area utilization in a large group of horses in a pasture-based housing system in Northern Germany. The movements of 40 ponies with established group affiliations were observed over approximately one month from late August to early October 2022 using GPS technology. Their dominance hierarchy was determined by testing each possible combination in separate pair-feeding tests. For the determination of area utilization, the available space was divided into a total of 8889 quadrants with 3x3 m each. Analysis of daily distances travelled by the horses revealed an average of 5.4 ± 1.2 km/day, with no significant effect of social rank. However, age had a significant effect, with older horses walking shorter distances. Quadrants used per hour, as measure of area utilization, averaged 37.9 ± 6.4, with no effect of social rank but a significant effect of age observed. Heat maps showed full pasture utilization, with lower ranked horses having less access to resources around the watering trough and a greater use of less attractive grazing areas. The study suggests that in this specific setting, social rank did not significantly affect locomotor behaviour or area use. Although the study did not find a correlation between dominance rank and locomotor behaviour or area use, it highlighted the need for further research in different group sizes and housing systems to optimise group housing practices for horses, especially in settings with limited space and resources.
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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

Journal Article

Gesa Weidling

Joachim Krieter

Ralf Lübben

Irena Czycholl

Horse

Dominance hierarchy

GPS

Distance

Area utilization

Group housing

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2025

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