IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography. - Alina Jade Barnett; Fides Regina Schwartz; Chaofan Tao; Chaofan Chen; Yinhao Ren; Joseph Y. Lo; Cynthia Rudin (CoRR, 2021)

Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning. - Alina Jade Barnett; Fides Regina Schwartz; Chaofan Tao; Chaofan Chen; Yinhao Ren; Joseph Y. Lo; Cynthia Rudin (CoRR, 2021)

A case-based interpretable deep learning model for classification of mass lesions in digital mammography. - Alina Jade Barnett; Fides Regina Schwartz; Chaofan Tao; Chaofan Chen; Yinhao Ren; Joseph Y. Lo; Cynthia Rudin (Nat. Mach. Intell., 2021)

Interpretable deep learning models for better clinician-AI communication in clinical mammography. - Alina Jade Barnett; Vaibhav Sharma; Neel Gajjar; Jerry Fang; Fides Regina Schwartz; Chaofan Chen; Joseph Y. Lo; Cynthia Rudin (Image Perception, Observer Performance, and Technology Assessment, 2022)

A user interface to communicate interpretable AI decisions to radiologists. - Yanchen Jessie Ou; Alina Jade Barnett; Anika Mitra; Fides Regina Schwartz; Chaofan Chen; Lars J. Grimm; Joseph Y. Lo; Cynthia Rudin (Image Perception, Observer Performance, and Technology Assessment, 2023)

A user interface to communicate interpretable AI decisions to radiologists (Erratum). - Yanchen Ou; Alina Jade Barnett; Anika Mitra; Fides Regina Schwartz; Chaofan Chen; Lars J. Grimm; Joseph Y. Lo; Cynthia Rudin (Image Perception, Observer Performance, and Technology Assessment, 2023)

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. - Julia Yang; Alina Jade Barnett; Jon Donnelly; Satvik Kishore; Jerry Fang; Fides Regina Schwartz; Chaofan Chen; Joseph Y. Lo; Cynthia Rudin (CVPR Workshops, 2024)