Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

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Bibliographic Details
Title: Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.
Authors: Shu HJ; Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan., Chang ST; Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan., Gameiro RR; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Mass.; Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Mass., Gichoya JW; Department of Radiology, Emory University, Atlanta, Ga., Celi LA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Mass.; Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Mass.; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Mass., Kuo PC; Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
Source: Radiology. Artificial intelligence [Radiol Artif Intell] 2026 Jun 24, pp. e250731. Date of Electronic Publication: 2026 Jun 24.
Publication Type: Journal Article
Journal Info: Publisher: Radiological Society of North America, Inc Country of Publication: United States NLM ID: 101746556 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2638-6100 (Electronic) Linking ISSN: 26386100 NLM ISO Abbreviation: Radiol Artif Intell Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2638-6100
DOI:10.1148/ryai.250731