This looks like that: deep learning for interpretable image recognition. - Chaofan Chen; Oscar Li; Alina Barnett; Jonathan Su; Cynthia Rudin (CoRR, 2018)

This Looks Like That: Deep Learning for Interpretable Image Recognition. - Chaofan Chen; Oscar Li; Daniel Tao; Alina Barnett; Cynthia Rudin; Jonathan Su (NeurIPS, 2019)

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)

Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes. - Jon Donnelly; Alina Jade Barnett; Chaofan Chen (CVPR, 2022)

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)

Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning. - Alina Jade Barnett; Zhicheng Guo; Jin Jing; Wendong Ge; Cynthia Rudin; M. Brandon Westover (CoRR, 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)

ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges. - Dennis Tang; Frank Willard; Ronan Tegerdine; Luke Triplett; Jon Donnelly; Luke Moffett; Lesia Semenova; Alina Jade Barnett; Jin Jing; Cynthia Rudin; M. Brandon Westover (CoRR, 2023)

Interpretable Machine Learning With Medical Applications. - Alina Jade Barnett (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)

This Looks Better than That: Better Interpretable Models with ProtoPNeXt. - Frank Willard; Luke Moffett; Emmanuel Mokel; Jon Donnelly; Stark Guo; Julia Yang; Giyoung Kim; Alina Jade Barnett; Cynthia Rudin (CoRR, 2024)

Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time. - Jon Donnelly; Zhicheng Guo; Alina Jade Barnett; Hayden McTavish; Chaofan Chen; Cynthia Rudin (CVPR, 2025)