  Deep learning enables the modelling of high-resolution histopathology
whole-slide images (WSI). Weakly supervised learning of tile-level data is
typically applied for tasks where labels only exist on the patient or WSI level
(e.g. patient outcomes or histological grading). In this context, there is a
need for improved spatial interpretability of predictions from such models. We
propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model
interpretation. It provides a principled yet straightforward way to establish
the spatial area of WSI required for assigning a particular prediction label.
We demonstrate WEEP on a binary classification task in the area of breast
cancer computational pathology. WEEP is easy to implement, is directly
connected to the model-based decision process, and offers information relevant
to both research and diagnostic applications.
