Prof. Dr.-Ing. Katharina Breininger



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Types of publications

Journal article
Book chapter / Article in edited volumes
Authored book
Translation
Thesis
Edited Volume
Conference contribution
Other publication type
Unpublished / Preprint

Publication year

From
To

Abstract

Journal

Stochastic latent feature distillation: Enhancing dataset distillation via structured uncertainty modeling (2025) Li Z, Cechnicka S, Ouyang C, Breininger K, Schüffler P, Kainz B Journal article Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling (2025) Possart D, Mill L, Vollnhals F, Hildebrand T, Suter P, Hoffmann M, Utz J, et al. Journal article A self-supervised multimodal deep learning approach to differentiate post-radiotherapy progression from pseudoprogression in glioblastoma (2025) Gomaa A, Huang Y, Stephan P, Breininger K, Frey B, Dörfler A, Schnell O, et al. Journal article Learning-based autonomous navigation, benchmark environments and simulation framework for endovascular interventions (2025) Karstensen L, Robertshaw H, Hatzl J, Jackson B, Langejürgen J, Breininger K, Uhl C, et al. Journal article Effortless Vision-Language Model Specialization in Histopathology without Annotation (2025) Qiu J, Jain N, Ammeling J, Aubreville M, Breininger K Journal article Fully automatic HER2 tissue segmentation for interpretable HER2 scoring (2025) Öttl M, Steenpaß J, Wilm F, Qiu J, Rübner M, Lang-Schwarz C, Taverna C, et al. Journal article Bildverarbeitung für die Medizin 2025 (2025) Palm C, Breininger K, Deserno TM, Handels H, Maier A, Maier-Hein KH, Tolxdorff T Edited Volume Abstract: Multi-level Cancer Profiling through Joint Cell-graph Representations (2025) Rivera Monroy LC, Rist L, Wilm F, Ostalecki C, Baur A, Vera González J, Breininger K, Maier A Conference contribution Abstract: Leveraging Image Captions for Selective Whole Slide Image Annotation (2025) Qiu J, Aubreville M, Wilm F, Öttl M, Utz J, Schlereth M, Breininger K Conference contribution Artificial intelligence can be trained to predict c-KIT-11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides (2025) Puget C, Ganz J, Ostermaier J, Conrad T, Parlak E, Bertram CA, Kiupel M, et al. Journal article