  Multiple Instance Learning (MIL) gains popularity in many real-life machine
learning applications due to its weakly supervised nature. However, the
corresponding effort on explaining MIL lags behind, and it is usually limited
to presenting instances of a bag that are crucial for a particular prediction.
In this paper, we fill this gap by introducing ProtoMIL, a novel
self-explainable MIL method inspired by the case-based reasoning process that
operates on visual prototypes. Thanks to incorporating prototypical features
into objects description, ProtoMIL unprecedentedly joins the model accuracy and
fine-grained interpretability, which we present with the experiments on five
recognized MIL datasets.
