  Identifying the type of kidney stones can allow urologists to determine their
formation cause, improving the early prescription of appropriate treatments to
diminish future relapses. However, currently, the associated ex-vivo diagnosis
(known as morpho-constitutional analysis, MCA) is time-consuming, expensive,
and requires a great deal of experience, as it requires a visual analysis
component that is highly operator dependant. Recently, machine learning methods
have been developed for in-vivo endoscopic stone recognition. Shallow methods
have been demonstrated to be reliable and interpretable but exhibit low
accuracy, while deep learning-based methods yield high accuracy but are not
explainable. However, high stake decisions require understandable
computer-aided diagnosis (CAD) to suggest a course of action based on
reasonable evidence, rather than merely prescribe one. Herein, we investigate
means for learning part-prototypes (PPs) that enable interpretable models. Our
proposal suggests a classification for a kidney stone patch image and provides
explanations in a similar way as those used on the MCA method.
