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

• URL: http://arxiv.org/abs/2107.05605v1
• Date: Mon, 12 Jul 2021 17:42:09 GMT
• Title: Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning
• Authors: Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin
• Abstract summary: We present a novel interpretable neural network algorithm that uses case-based reasoning for mammography. Our network presents both a prediction of malignancy and an explanation of that prediction using known medical features.
• Score: 20.665935997959025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When we deploy machine learning models in high-stakes medical settings, we must ensure these models make accurate predictions that are consistent with known medical science. Inherently interpretable networks address this need by explaining the rationale behind each decision while maintaining equal or higher accuracy compared to black-box models. In this work, we present a novel interpretable neural network algorithm that uses case-based reasoning for mammography. Designed to aid a radiologist in their decisions, our network presents both a prediction of malignancy and an explanation of that prediction using known medical features. In order to yield helpful explanations, the network is designed to mimic the reasoning processes of a radiologist: our network first detects the clinically relevant semantic features of each image by comparing each new image with a learned set of prototypical image parts from the 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 its prediction, and is better able to differentiate the classification-relevant parts of the image.

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