Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling. (arXiv:2206.08885v2 [eess.IV] UPDATED)

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 …

arxiv deep learning pooling variance weakly-supervised