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When we deploy machine learning models in high-stakes medical settings, wemust ensure these models make accurate predictions that are consistent withknown medical science. Inherently interpretable networks address this need byexplaining the rationale behind each decision while maintaining equal or higheraccuracy compared to black-box models. In this work, we present a novelinterpretable neural network algorithm that uses case-based reasoning formammography. Designed to aid a radiologist in their decisions, our networkpresents both a prediction of malignancy and an explanation of that predictionusing known medical features. In order to yield helpful explanations, thenetwork is designed to mimic the reasoning processes of a radiologist: ournetwork first detects the clinically relevant semantic features of each imageby comparing each new image with a learned set of prototypical image parts fromthe training images, then uses those clinical features to predict malignancy.Compared to other methods, our model detects clinical features (mass margins)with equal or higher accuracy, provides a more detailed explanation of itsprediction, and is better able to differentiate the classification-relevantparts of the image.

Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning