  Deep learning models have achieved high performance in medical applications,
however, their adoption in clinical practice is hindered due to their black-box
nature. Self-explainable models, like prototype-based models, can be especially
beneficial as they are interpretable by design. However, if the learnt
prototypes are of low quality then the prototype-based models are as good as
black-box. Having high quality prototypes is a pre-requisite for a truly
interpretable model. In this work, we propose a prototype evaluation framework
for coherence (PEF-C) for quantitatively evaluating the quality of the
prototypes based on domain knowledge. We show the use of PEF-C in the context
of breast cancer prediction using mammography. Existing works on
prototype-based models on breast cancer prediction using mammography have
focused on improving the classification performance of prototype-based models
compared to black-box models and have evaluated prototype quality through
anecdotal evidence. We are the first to go beyond anecdotal evidence and
evaluate the quality of the mammography prototypes systematically using our
PEF-C. Specifically, we apply three state-of-the-art prototype-based models,
ProtoPNet, BRAIxProtoPNet++ and PIP-Net on mammography images for breast cancer
prediction and evaluate these models w.r.t. i) classification performance, and
ii) quality of the prototypes, on three public datasets. Our results show that
prototype-based models are competitive with black-box models in terms of
classification performance, and achieve a higher score in detecting ROIs.
However, the quality of the prototypes are not yet sufficient and can be
improved in aspects of relevance, purity and learning a variety of prototypes.
We call the XAI community to systematically evaluate the quality of the
prototypes to check their true usability in high stake decisions and improve
such models further.
