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Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance.

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Bibliographic Details
Title: Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance.
Authors: Gabriel RM; School of Electrical and Computer Engineering, Georgia Institute of Technology, 791 Atlantic Dr NW, Atlanta, GA, 30332, USA., Zandehshahvar M; School of Electrical and Computer Engineering, Georgia Institute of Technology, 791 Atlantic Dr NW, Atlanta, GA, 30332, USA., van Assen M; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA., Kittisut N; School of Electrical and Computer Engineering, Georgia Institute of Technology, 791 Atlantic Dr NW, Atlanta, GA, 30332, USA., Peters K; College of Computing, Georgia Institute of Technology, Atlanta, GA, USA., De Cecco CN; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA., Adibi A; School of Electrical and Computer Engineering, Georgia Institute of Technology, 791 Atlantic Dr NW, Atlanta, GA, 30332, USA. ali.adibi@ece.gatech.edu.
Source: Journal of imaging informatics in medicine [J Imaging Inform Med] 2025 Dec 10. Date of Electronic Publication: 2025 Dec 10.
Publication Type: Journal Article
Journal Info: Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2948-2933
DOI:10.1007/s10278-025-01752-8