Work

Improving Fairness of Automated Chest Radiograph Diagnosis by Contrastive Learning

Year: 2024

Type: article

Abstract: To develop an artificial intelligence model that uses supervised contrastive learning (SCL) to minimize bias in chest radiograph diagnosis.

Source: Radiology Artificial Intelligence

DOI: 10.1148/ryai.230342

Authors: Mingquan Lin, Tianhao Li, Zhaoyi Sun, Gregory Holste, Ying Ding, Fei Wang, George Shih, Yifan Peng

Institutions: University of Minnesota, Weill Cornell Medicine, The University of Texas at Austin

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, Advanced Research Projects Agency, National Heart, Lung, and Blood Institute, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine

Awards: G1580422627, CAREER: Knowledge-enhanced and interpretable radiology report generation, G443831456, G5838401220, G6671297155, G691527071, G9000103234

OpenAlex ID: W4401793728

FWCI: 1.197

Cites: 24

Cited by: 15

Related to: 10