Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography

Authors

MinJae Woo, Clemson University
Linglin Zhang, Equifax Inc.
Beatrice Brown-Mulry, Emory University
InChan Hwang, Montreat College
Judy Wawira Gichoya, Emory University
Aimilia Gastounioti, Washington University School of Medicine in St. Louis
Imon Banerjee, Arizona State University
Laleh Seyyed-Kalantari, York University
Hari Trivedi, Emory University

Journal

PLOS Digital Health

Publication Date

4-1-2025

Volume

4

Issue

4

First Page

e0000811

Document Type

Open Access Publication

DOI

10.1371/journal.pdig.0000811

Rights and Permissions

Woo M, Zhang L, Brown-Mulry B, Hwang I, Gichoya JW, Gastounioti A, Banerjee I, Seyyed-Kalantari L, Trivedi H. Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography. PLOS Digit Health. 2025 Apr 8;4(4):e0000811. doi: 10.1371/journal.pdig.0000811. © 2025 Woo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Recommended Citation

Woo, MinJae; Zhang, Linglin; Brown-Mulry, Beatrice; Hwang, InChan; Gichoya, Judy Wawira; Gastounioti, Aimilia; Banerjee, Imon; Seyyed-Kalantari, Laleh; and Trivedi, Hari, "Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography." PLOS Digital Health. 4, 4. e0000811 (2025).
https://digitalcommons.wustl.edu/oa_4/5235

Department

ICTS (Institute of Clinical and Translational Sciences)

Additional Links

Supplemental material is available for this article at publisher site.