  In this paper, we propose a novel interpretation method tailored to
histological Whole Slide Image (WSI) processing. A Deep Neural Network (DNN),
inspired by Bag-of-Features models is equipped with a Multiple Instance
Learning (MIL) branch and trained with weak supervision for WSI classification.
MIL avoids label ambiguity and enhances our model's expressive power without
guiding its attention. We utilize a fine-grained logit heatmap of the models
activations to interpret its decision-making process. The proposed method is
quantitatively and qualitatively evaluated on two challenging histology
datasets, outperforming a variety of baselines. In addition, two expert
pathologists were consulted regarding the interpretability provided by our
method and acknowledged its potential for integration into several clinical
applications.
