Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

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第一作者： Anusua,Trivedi

第一单位： AI for Good Research Lab, Microsoft, Redmond, WA, United States of America.;Flipkart US R&D, Seattle, WA, United States of America.

作者： Anusua,Trivedi ^[1] ; Caleb,Robinson ^[2] ; Marian,Blazes ^[3] ; Anthony,Ortiz ^[2] ; Jocelyn,Desbiens ^[4] ; Sunil,Gupta ^[4] ; Rahul,Dodhia ^[2] ; Pavan K,Bhatraju ^[5] ; W Conrad,Liles ^[6] ; Jayashree,Kalpathy-Cramer ^[7] ; Aaron Y,Lee ^[3] ; Juan M,Lavista Ferres ^[2]

作者单位： AI for Good Research Lab, Microsoft, Redmond, WA, United States of America.;Flipkart US R&D, Seattle, WA, United States of America. ^[1] AI for Good Research Lab, Microsoft, Redmond, WA, United States of America. ^[2] Department of Ophthalmology, University of Washington, Seattle, WA, United States of America. ^[3] Intelligent Retinal Imaging Systems, Pensacola, FL, United States of America. ^[4] Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, WA, United States of America.;Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, United States of America. ^[5] Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, WA, United States of America. ^[6] Neuroscience, Massachusetts General Hospital, Boston, MA, United States of America. ^[7]

主题词人类(Humans);肺(Lung);放射摄影术, 胸部(Radiography, Thoracic);X线(X-Rays)

DOI 10.1371/journal.pone.0274098

PMID 36201483

发布时间 2024-09-23

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