Interpretable deep learning models for better clinician-AI communication in clinical mammography

Alina Jade Barnett; Vaibhav Sharma; Neel Gajjar; Jerry Fang; Fides Schwartz M.D.; Chaofan Chen; Joseph Y. Lo; Cynthia Rudin

doi:10.1117/12.2612372

4 April 2022 Interpretable deep learning models for better clinician-AI communication in clinical mammography

Alina Jade Barnett, Vaibhav Sharma, Neel Gajjar, Jerry Fang, Fides Schwartz M.D., Chaofan Chen, Joseph Y. Lo, Cynthia Rudin

Author Affiliations +

Proceedings Volume 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment; 1203507 (2022) https://doi.org/10.1117/12.2612372
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

There is increasing interest in using deep learning and computer vision to help guide clinical decisions, such as whether to order a biopsy based on a mammogram. Existing networks are typically black box, unable to explain how they make their predictions. We present an interpretable deep-learning network which explains its predictions in terms of BI-RADS features mass shape and mass margin. Our model predicts mass margin and mass shape, then uses the logits from those interpretable models to predict malignancy, also using an interpretable model. The interpretable mass margin model explains its predictions using a prototypical parts model. The interpretable mass shape model predicts segmentations, fits an ellipse, then determines shape based on the goodness of fit and eccentricity of the fitted ellipse. While including mass shape logits in the malignancy prediction model did not improve performance, we present this technique as part of a framework for better clinician-AI communication.

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Citation Download Citation

Alina Jade Barnett, Vaibhav Sharma, Neel Gajjar, Jerry Fang, Fides Schwartz M.D., Chaofan Chen, Joseph Y. Lo, and Cynthia Rudin "Interpretable deep learning models for better clinician-AI communication in clinical mammography", Proc. SPIE 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 1203507 (4 April 2022); https://doi.org/10.1117/12.2612372

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