Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Capellán-Martín, Daniel Parida, Abhijeet Gómez-Valverde, Juan J. Sanchez-Jacob, Ramon Roshanitabrizi, Pooneh Linguraru, Marius G. Ledesma-Carbayo, María J. Anwar, Syed M. |
| author_facet | Capellán-Martín, Daniel Parida, Abhijeet Gómez-Valverde, Juan J. Sanchez-Jacob, Ramon Roshanitabrizi, Pooneh Linguraru, Marius G. Ledesma-Carbayo, María J. Anwar, Syed M. |
| contents | 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$13.4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i.e., non pre-trained) ViT models, achieving top performances of 0.959 AUC and 0.962 AUPR in adult TB detection, and 0.697 AUC and 0.607 AUPR in zero-shot pediatric TB detection. As a result, this work demonstrates that self-supervised learning on adult CXRs effectively extends to challenging downstream tasks such as pediatric TB detection, where data are scarce. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14741 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning Capellán-Martín, Daniel Parida, Abhijeet Gómez-Valverde, Juan J. Sanchez-Jacob, Ramon Roshanitabrizi, Pooneh Linguraru, Marius G. Ledesma-Carbayo, María J. Anwar, Syed M. Computer Vision and Pattern Recognition Image and Video Processing Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driven computer-aided detection (CAD) tools, especially those utilizing deep learning, show promise in enhancing lung disease detection. However, challenges include data scarcity and lack of generalizability. In this context, we propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection. We demonstrate improvements in TB detection performance ($\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. |
| title | Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2402.14741 |