Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.

ABSTRACT: Deep learning models are increasingly being used to interpret whole-slide images (WSIs) in digital pathology and to predict genetic mutations. Currently, it is commonly assumed that tumor regions have most of the predictive power. However, it is reasonable to assume that other tissues from the tumor microenvironment may also provide important predictive information. In this paper, we propose an unsupervised clustering-based multiple-instance deep learning model for the prediction of genetic mutations using WSIs of three cancer types obtained from The Cancer Genome Atlas. Our proposed model facilitates the identification of spatial regions related to specific gene mutations and exclusion of patches that lack predictive information through the use of unsupervised clustering. This results in

SUBMITTER: Chen Z 

PROVIDER: S-EPMC9732687 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature