A Framework for Interpretability in Machine Learning for Medical Imaging-Reference-Cited by-同舟云学术

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A Framework for Interpretability in Machine Learning for Medical Imaging

Published:2024 Issue: Volume:12 Page:53277-53292
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wang Alan Q.¹^ORCID,Karaman Batuhan K.¹^ORCID,Kim Heejong²^ORCID,Rosenthal Jacob²^ORCID,Saluja Rachit¹,Young Sean I.³^ORCID,Sabuncu Mert R.¹

Affiliation:

1. School of Electrical and Computer Engineering, Cornell University-Cornell Tech, New York City, NY, USA

2. Department of Radiology, Weill Cornell Medical School, New York City, NY, USA

3. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA

Funder

NIH

NSF CAREER

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/6287639/10380310/10496678-aam.pdf

Reference160 articles.

1. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study

2. Machine Learning for Medical Imaging

3. The Current and Future State of AI Interpretation of Medical Images

4. On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities

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