Publikationen

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
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  FLAIR - Flensburg Artificial Intelligence Research
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  Informatik und Gestaltung
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  Data Science und Künstliche Intelligenz
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  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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  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
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  CIVU – Center for Interaction, Visualization and Usability
  
  FLAIR - Flensburg Artificial Intelligence Research
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  Gesundheit und Life Sciences
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  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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  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
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  Gesundheit und Life Sciences
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  Data Science und Künstliche Intelligenz
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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
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  Aubreville, Marc
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  (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
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  Informatik und Gestaltung
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  Data Science und Künstliche Intelligenz
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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
  
  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
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  Gesundheit und Life Sciences
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  Digitale Transformation
  
  Human-Computer-Interaction
  
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  Fachbereich 3: Information und Kommunikation
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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
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  Optical Biopsy of Sinonasal Tumors using Confocal Laser Endomicroscopy: A Clinical and Deep Learning-based Assessment and Visualization
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  FLAIR - Flensburg Artificial Intelligence Research
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  Data Science und Künstliche Intelligenz
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  Fachbereich 3: Information und Kommunikation
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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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- Personenseiten
  
  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
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  Informatik und Gestaltung
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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 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
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  FLAIR - Flensburg Artificial Intelligence Research
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  Informatik und Gestaltung
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  Digitalisierung im Gesundheitswesen
  
  Data Science und Künstliche Intelligenz
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  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 2026)
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  Aubreville, Marc
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  FLAIR - Flensburg Artificial Intelligence Research
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  Digitalisierung im Gesundheitswesen
  
  Data Science und Künstliche Intelligenz
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  Fachbereich 3: Information und Kommunikation
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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
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  FLAIR - Flensburg Artificial Intelligence Research
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  Data Science und Künstliche Intelligenz
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