Joint and individual analysis of breast cancer histologic images and genomic covariates

Iain Carmichael

Abstract:

Supervised learning tasks such as cancer survival prediction
from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment. These learning tasks are often solved with deep
multi-instance learning (MIL) models that do not explicitly capture intratumoral heterogeneity. We develop a novel variance pooling architecture that enables a MIL model to incorporate intratumoral heterogeneity
into its predictions. Two interpretability tools based on “representative
patches” are illustrated to probe the biological signals captured by these
models. An empirical study with 4,479 gigapixel WSIs from the Cancer
Genome Atlas shows that adding variance pooling onto MIL frameworks
improves survival prediction performance for five cancer types.