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

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Analyses of non-coding somatic drivers in 2,658 cancer whole genomes., , , , , , , , , and 179 other author(s). Nat., 578 (7793): 102-111 (2020)Comparing Algorithms That Reconstruct Cell Lineage Trees Utilizing Information on Microsatellite Mutations., , , , , , and . PLoS Comput. Biol., (2013)Biologically-primed deep neural network improves colorectal Cancer Molecular subtypes prediction from H&E stained images., , , , and . CoRR, (2023)Patient-Level Microsatellite Stability Assessment from Whole Slide Images by Combining Momentum Contrast Learning and Group Patch Embeddings., , , , and . ECCV Workshops (3), volume 13803 of Lecture Notes in Computer Science, page 454-465. Springer, (2022)An Efficient and Accurate Neural Network Tool for Finding Correlation Between Gene Expression and Histological Images., , and . CLIP/FAIMI/EPIMI@MICCAI, volume 14242 of Lecture Notes in Computer Science, page 81-88. Springer, (2023)Polymorphism Data Can Reveal the Origin of Species Abundance Statistics., and . PLoS Comput. Biol., (2009)CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images., , , , and . CoRR, (2024)