  State-of-the-art (SOTA) deep learning mammogram classifiers, trained with
weakly-labelled images, often rely on global models that produce predictions
with limited interpretability, which is a key barrier to their successful
translation into clinical practice. On the other hand, prototype-based models
improve interpretability by associating predictions with training image
prototypes, but they are less accurate than global models and their prototypes
tend to have poor diversity. We address these two issues with the proposal of
BRAIxProtoPNet++, which adds interpretability to a global model by ensembling
it with a prototype-based model. BRAIxProtoPNet++ distills the knowledge of the
global model when training the prototype-based model with the goal of
increasing the classification accuracy of the ensemble. Moreover, we propose an
approach to increase prototype diversity by guaranteeing that all prototypes
are associated with different training images. Experiments on weakly-labelled
private and public datasets show that BRAIxProtoPNet++ has higher
classification accuracy than SOTA global and prototype-based models. Using
lesion localisation to assess model interpretability, we show BRAIxProtoPNet++
is more effective than other prototype-based models and post-hoc explanation of
global models. Finally, we show that the diversity of the prototypes learned by
BRAIxProtoPNet++ is superior to SOTA prototype-based approaches.
