B. Dislich

B. Dislich is affiliated with the German Cancer Research Center (DKFZ), contributing to cutting-edge research in computational pathology and artificial intelligence applications for cancer diagnostics. As a Researcher, Dislich participates in developing deep learning methodologies for histopathological slide analysis.

Dislich's research focuses on weakly-supervised learning pipelines for whole slide image classification, addressing critical challenges in computational pathology where ground truth annotations exist at the slide level rather than individual tiles. This work bridges AI innovation with clinical pathology needs, particularly in biomarker detection and cancer subtyping.

The publications reveal a strong emphasis on methodological rigor in medical AI validation, with research spanning neural network architectures optimized for histopathological data. Dislich's contributions support the broader DKFZ mission of translating computational advances into clinical diagnostic tools.

While specific advising roles or grants aren't documented in the available records, the collaborative nature of the publications (with multi-institutional co-authors including Loeffler, Muti, and Ghaffari Laleh) indicates participation in DKFZ's interdisciplinary cancer research ecosystem.