Showing eess.IVShow all
2 papers · 1 filter
eess.IV2024
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology
Peter Neidlinger, Omar S. M. El Nahhas, Hannah Sophie Muti +13
Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information. However, there is…
eess.IV2024
Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology
Omar S. M. El Nahhas, Georg Wölflein, Marta Ligero +5
Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting. Recently, the prediction of continuous biomarkers through regress…