Figure 1.

Overview of proposed integrated survival deep learning model. (1) Multiple regions of interest are extracted from the whole slide image of H&E stained tumor tissue containing viable tumor. (2) These regions are then sent through a network of convolutional, pooling and fully connected layers that extract survival discriminative features. A Cox proportional hazards model was integrated with the fully connected layer which outputs patient specific risk scores. (3) Survival risk grouping: Recursive partitioning analysis was employed for risk stratification of the patient cohort based on predicted risk scores and prognostic molecular variables. H&E, hematoxylin and eosin.