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

A. Barnett, V. Sharma, N. Gajjar, J. Fang, F. Schwartz, C. Chen, J. Lo, and C. Rudin. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 12035 of SPIE Proceedings, SPIE, (2022)

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Klaus Jäde

Susanne Jäde

Kathrin Jäde

Alireza Alinaghi

Kerstin Barnett

Other publications of authors with the same name

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography.A. Barnett, F. Schwartz, C. Tao, C. Chen, Y. Ren, J. Lo, and C. Rudin. CoRR, (2021)A user interface to communicate interpretable AI decisions to radiologists.Y. Ou, A. Barnett, A. Mitra, F. Schwartz, C. Chen, L. Grimm, J. Lo, and C. Rudin. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 12467 of SPIE Proceedings, SPIE, (2023)Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning.A. Barnett, Z. Guo, J. Jing, W. Ge, C. Rudin, and M. Westover. CoRR, (2022)Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes.J. Donnelly, A. Barnett, and C. Chen. CVPR, page 10255-10265. IEEE, (2022)Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning.A. Barnett, F. Schwartz, C. Tao, C. Chen, Y. Ren, J. Lo, and C. Rudin. CoRR, (2021)ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges.D. Tang, F. Willard, R. Tegerdine, L. Triplett, J. Donnelly, L. Moffett, L. Semenova, A. Barnett, J. Jing, C. Rudin and 1 other author(s). CoRR, (2023)Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes.J. Donnelly, A. Barnett, and C. Chen. CoRR, (2021)Interpretable Machine Learning With Medical Applications.A. Barnett. Duke University, Durham, NC, USA, (2023)base-search.net (ftdukeunivdsp:oai:localhost:10161/29171).Interpretable deep learning models for better clinician-AI communication in clinical mammography.A. Barnett, V. Sharma, N. Gajjar, J. Fang, F. Schwartz, C. Chen, J. Lo, and C. Rudin. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 12035 of SPIE Proceedings, SPIE, (2022)A case-based interpretable deep learning model for classification of mass lesions in digital mammography.A. Barnett, F. Schwartz, C. Tao, C. Chen, Y. Ren, J. Lo, and C. Rudin. Nat. Mach. Intell., 3 (12): 1061-1070 (2021)

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Disambiguation of "Barnett, Alina Jade"

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

Please choose a person to relate this publication to

Klaus Jäde

Susanne Jäde

Kathrin Jäde

Alireza Alinaghi

Kerstin Barnett

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Barnett, Alina Jade"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Interpretable deep learning models for better clinician-AI communication in clinical mammography.

Please choose a person to relate this publication to

Klaus Jäde

Susanne Jäde

Kathrin Jäde

Alireza Alinaghi

Kerstin Barnett

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

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