Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance.
Saved in:
| 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 |
Be the first to leave a comment!
