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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

(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
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
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
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
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

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
An Interdisciplinary Perspective on AI-Supported Decision Making in Medicine

Ammeling, J., Aubreville, M., Fritz, A., Kießig, A., Krügel, S., & Uhl, M. (2024). An Interdisciplinary Perspective on AI-Supported Decision Making in Medicine. Technology in Society, 102791.
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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

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Informatik und Gestaltung

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonas Ammeling

Marc Aubreville

Alexis Fritz

Angelika Kießig

Sebastian Krügel

Matthias Uhl
Breast cancer survival prediction using an automated mitosis detection pipeline

Stathonikos, N., Aubreville, M., de Vries, S., Wilm, F., Bertram, C. A., Veta, M., & van Diest, P. J. (2024). Breast cancer survival prediction using an automated mitosis detection pipeline. The Journal of Pathology: Clinical Research, 10, e70008. http://doi.org/https://doi.org/10.1002/2056-4538.70008

Abstract

Abstract Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.
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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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2024

Journal Article

Nikolas Stathonikos

Marc Aubreville

Sjoerd de Vries

Frauke Wilm

Christof Bertram

Mitko Veta

Paul van Diest

artificial intelligence

machine learning

histopathology

prognosis

mitosis

outcome

deep learning
Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

Ganz, J., Marzahl, C., Ammeling, J., Rosbach, E., Richter, B., Puget, C., … Aubreville, M. (2024). Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis. Scientific Reports, 14, 26273. http://doi.org/10.1038/s41598-024-77244-6

Abstract

Abstract The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.
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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

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Informatik und Gestaltung

Forschungsfeld

Human-Computer-Interaction

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonathan Ganz

Christian Marzahl

Jonas Ammeling

Emely Rosbach

Barbara Richter

Chloé Puget

Daniela Denk

Elena Demeter

Flaviu Tăbăran

Gabriel Wasinger

Karoline Lipnik

Marco Tecilla

Matthew Valentine

Michael Dark

Niklas Abele

Pompei Bolfa

Ramona Erber

Robert Klopfleisch

Sophie Merz

Taryn Donovan

Samir Jabari

Christof Bertram

Katharina Breininger

Marc Aubreville

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2025

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2024

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