A method for spatial interpretation of weakly supervised deep learning models in computational pathology

Abstract

Abstract 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 the weakly supervised learning context, there is a need for a methodology that facilitates the identification of the precise spatial regions in WSI that drive the prediction of the slide label. Such information is also needed for any further spatial interpretation 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 facilitates the identification of spatial regions in WSI that are driving the decision making of a particular weakly supervised learning model, which can be further visualised and analysed to provide spatial interpretability of the model. The method is easy to implement, is directly connected to the model-based decision process, and offers information relevant to both research and diagnostic applications.