  Multiple Instance Learning (MIL) has been widely applied in pathology towards
solving critical problems such as automating cancer diagnosis and grading,
predicting patient prognosis, and therapy response. Deploying these models in a
clinical setting requires careful inspection of these black boxes during
development and deployment to identify failures and maintain physician trust.
In this work, we propose a simple formulation of MIL models, which enables
interpretability while maintaining similar predictive performance. Our Additive
MIL models enable spatial credit assignment such that the contribution of each
region in the image can be exactly computed and visualized. We show that our
spatial credit assignment coincides with regions used by pathologists during
diagnosis and improves upon classical attention heatmaps from attention MIL
models. We show that any existing MIL model can be made additive with a simple
change in function composition. We also show how these models can debug model
failures, identify spurious features, and highlight class-wise regions of
interest, enabling their use in high-stakes environments such as clinical
decision-making.
