Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA with Less Data using Swin Transformer. (arXiv:2208.10495v1 [q-bio.QM])

Artificial intelligence (AI) models have been developed for predicting
clinically relevant biomarkers, including microsatellite instability (MSI), for
colorectal cancers (CRC). However, the current deep-learning networks are
data-hungry and require large training datasets, which are often lacking in the
medical domain. In this study, based on the latest Hierarchical Vision
Transformer using Shifted Windows (Swin-T), we developed an efficient workflow
for biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island
methylator phenotype, BRAF, and TP53 mutation) that only required relatively
small …

arxiv bio cancer data images sota swin transformer