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Found 3 papers, 2 papers with code
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
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.
A Lightweight, Rapid and Efficient Deep Convolutional Network for Chest X-Ray Tuberculosis Detection
In this work, we propose LightTBNet, a novel lightweight, fast and efficient deep convolutional network specially customized to detect TB from CXR images.
Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis
In clinical practice, experienced physicians assess TB by examining chest X-rays (CXR).
