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Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
- Source :
- 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece
- Publication Year :
- 2024
-
Abstract
- Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driven computer-aided detection (CAD) tools, especially those utilizing deep learning, show promise in enhancing lung disease detection. However, challenges include data scarcity and lack of generalizability. In this context, we propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection. We demonstrate improvements in TB detection performance ($\sim$12.7% and $\sim$13.4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i.e., non pre-trained) ViT models, achieving top performances of 0.959 AUC and 0.962 AUPR in adult TB detection, and 0.697 AUC and 0.607 AUPR in zero-shot pediatric TB detection. As a result, this work demonstrates that self-supervised learning on adult CXRs effectively extends to challenging downstream tasks such as pediatric TB detection, where data are scarce.<br />Comment: 5 pages, 3 figures, 2 tables. This paper has been accepted at IEEE ISBI 2024
Details
- Database :
- arXiv
- Journal :
- 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece
- Publication Type :
- Report
- Accession number :
- edsarx.2402.14741
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/ISBI56570.2024.10635520