  Multiple Instance Learning (MIL) models have proven effective for cancer
prognosis from Whole Slide Images. However, the original MIL formulation
incorrectly assumes the patches of the same image to be independent, leading to
a loss of spatial context as information flows through the network.
Incorporating contextual knowledge into predictions is particularly important
given the inclination for cancerous cells to form clusters and the presence of
spatial indicators for tumors. State-of-the-art methods often use attention
mechanisms eventually combined with graphs to capture spatial knowledge. In
this paper, we take a novel and transversal approach, addressing this issue
through the lens of regularization. We propose Context-Aware Regularization for
Multiple Instance Learning (CARMIL), a versatile regularization scheme designed
to seamlessly integrate spatial knowledge into any MIL model. Additionally, we
present a new and generic metric to quantify the Context-Awareness of any MIL
model when applied to Whole Slide Images, resolving a previously unexplored gap
in the field. The efficacy of our framework is evaluated for two survival
analysis tasks on glioblastoma (TCGA GBM) and colon cancer data (TCGA COAD).
