Leveraging Masked Autoencoders for Enhanced Classification of Multi-labeled Pediatric Thoracic Diseases in Chest X-rays
Author:
Affiliation:
1. Inje University,Department of Healthcare Information Technology,Gimhae-si,Republic of Korea
2. Inje University,Department of Medical Information Technology,Gimhae-si,Republic of Korea
Funder
National Research Foundation of Korea (NRF)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10628105/10628121/10628314.pdf?arnumber=10628314
Reference9 articles.
1. Pediatric Chest Disorders: Practical Imaging Approach to Diagnosis
2. Diagnosis of common pulmonary diseases in children by X-ray images and deep learning
3. Enhancing pediatric pneumonia diagnosis through masked autoencoders
4. ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
5. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
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