CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H\&E stained images

Autor: Hezi, Hadar, Gelber, Matan, Balabanov, Alexander, Maruvka, Yosef E., Freiman, Moti

Treatment approaches for colorectal cancer (CRC) are highly dependent on the molecular subtype, as immunotherapy has shown efficacy in cases with microsatellite instability (MSI) but is ineffective for the microsatellite stable (MSS) subtype. There i

Externí odkaz: http://arxiv.org/abs/2401.16131

CIMIL-CRC: A clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images.

Autor: Hezi H; Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel., Gelber M; Faculty of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa, Israel., Balabanov A; Faculty of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa, Israel., Maruvka YE; Faculty of Food Engineering and Biotechnology, Technion - Israel Institute of Technology, Haifa, Israel., Freiman M; Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel. Electronic address: moti.freiman@technion.ac.il.

Publikováno v: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2024 Nov 19; Vol. 259, pp. 108513. Date of Electronic Publication: 2024 Nov 19.