  Interpretability is a key requirement for the use of machine learning models
in high-stakes applications, including medical diagnosis. Explaining black-box
models mostly relies on post-hoc methods that do not faithfully reflect the
model's behavior. As a remedy, prototype-based networks have been proposed, but
their interpretability is limited as they have been shown to provide coarse,
unreliable, and imprecise explanations. In this work, we introduce
Proto-BagNets, an interpretable-by-design prototype-based model that combines
the advantages of bag-of-local feature models and prototype learning to provide
meaningful, coherent, and relevant prototypical parts needed for accurate and
interpretable image classification tasks. We evaluated the Proto-BagNet for
drusen detection on publicly available retinal OCT data. The Proto-BagNet
performed comparably to the state-of-the-art interpretable and
non-interpretable models while providing faithful, accurate, and clinically
meaningful local and global explanations. The code is available at
https://github.com/kdjoumessi/Proto-BagNets.
