6 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype
Jan-Philipp Redlich, Friedrich Feuerhake, Stefan Nikolin +12
Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant brain tumor. While histomorphology is a crucial component of GBM-IDHwt diagnosis, it is not further considered f…
Continual Domain Incremental Learning for Privacy-aware Digital Pathology
Pratibha Kumari, Daniel Reisenbüchler, Lucas Luttner +3
In recent years, there has been remarkable progress in the field of digital pathology, driven by the ability to model complex tissue patterns using advanced deep-learning algorithm…
Tissue Concepts: supervised foundation models in computational pathology
Till Nicke, Jan Raphael Schaefer, Henning Hoefener +4
Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Founda…
Unsupervised Latent Stain Adaptation for Computational Pathology
Daniel Reisenbüchler, Lucas Luttner, Nadine S. Schaadt +2
In computational pathology, deep learning (DL) models for tasks such as segmentation or tissue classification are known to suffer from domain shifts due to different staining techn…