Frauke Wilm

Juniorprofessur für Artificial Intelligence in Medical Imaging Lehrstuhl für Informatik 5 (Mustererkennung) Department Artificial Intelligence in Biomedical Engineering (AIBE)

Style-Extracting Diffusion Models for Semi-supervised Histopathology Segmentation (2025) Öttl M, Wilm F, Steenpaß J, Qiu J, Rübner M, Hartmann A, Beckmann M, et al. Conference contribution, Original article Author Correction: A comprehensive multi-domain dataset for mitotic figure detection (Scientific Data, (2023), 10, 1, (484), 10.1038/s41597-023-02327-4) (2024) Aubreville M, Wilm F, Stathonikos N, Breininger K, Donovan TA, Jabari S, Veta M, et al. Journal article, Erratum Breast cancer survival prediction using an automated mitosis detection pipeline (2024) Stathonikos N, Aubreville M, de Vries S, Wilm F, Bertram CA, Veta M, van Diest PJ Journal article Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge (2024) Aubreville M, Stathonikos N, Donovan TA, Klopfleisch R, Ammeling J, Ganz J, Wilm F, et al. Journal article Multi-level cancer profiling through joint cell-graph representations (2024) Rivera Monroy L, Rist L, Wilm F, Ostalecki C, Baur A, Vera González J, Breininger K, Maier A Journal article Appearance-based Debiasing of Deep Learning Models in Medical Imaging (2024) Wilm F, Reimann MK, Taubmann O, Mühlberg A, Breininger K Conference contribution, Conference Contribution Leveraging Image Captions for Selective Whole Slide Image Annotation (2024) Qiu J, Aubreville M, Wilm F, Öttl M, Utz J, Schlereth M, Breininger K Conference contribution A comprehensive multi-domain dataset for mitotic figure detection (2023) Aubreville M, Wilm F, Stathonikos N, Breininger K, Donovan TA, Jabari S, Veta M, et al. Journal article Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation (2023) Qiu J, Wilm F, Öttl M, Schlereth M, Liu C, Heimann T, Aubreville M, Breininger K Conference contribution, Conference Contribution Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry (2023) Wilm F, Ihling C, Méhes G, Terracciano L, Puget C, Klopfleisch R, Schüffler P, et al. Journal article