  Digital mammography is essential to breast cancer detection, and deep
learning offers promising tools for faster and more accurate mammogram
analysis. In radiology and other high-stakes environments, uninterpretable
("black box") deep learning models are unsuitable and there is a call in these
fields to make interpretable models. Recent work in interpretable computer
vision provides transparency to these formerly black boxes by utilizing
prototypes for case-based explanations, achieving high accuracy in applications
including mammography. However, these models struggle with precise feature
localization, reasoning on large portions of an image when only a small part is
relevant. This paper addresses this gap by proposing a novel multi-scale
interpretable deep learning model for mammographic mass margin classification.
Our contribution not only offers an interpretable model with reasoning aligned
with radiologist practices, but also provides a general architecture for
computer vision with user-configurable prototypes from coarse- to fine-grained
prototypes.
