Cleo-Aron Weis

Abstract Current deep learning models for classifying glomeruli in nephropathology are trained almost exclusively in a supervised manner, requiring expert-labeled images. Very little is known about the potential for unsupervised learning to overcome this bottleneck. To address this open question in a proof-of-concept, the project focused on the most fundamental classification task: globally sclerosed versus non-globally sclerosed glomeruli.

Open AccessArticle by 1,2,3,†, 1,†, 4,‡, 5, 1, 1,2,3, 3,6, 3,5, 3,4 and 1,2,3,* 1 Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163 Mannheim, Germany 2 Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack-Str.

Abstract Complex molecular alterations underlying cancer pathophysiology are intensely studied with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and immuno-histochemistry (IHC) of few marker proteins is currently the main clinical focus.