  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.
