Thorax Disease

Thorax disease research focuses on developing accurate and efficient computer-aided diagnosis systems using chest X-rays, primarily addressing challenges posed by limited labeled data, data privacy concerns, and class imbalances in disease prevalence. Current research employs deep learning models, including convolutional neural networks and vision transformers, often enhanced by techniques like low-rank feature learning, data-free distillation, and attention mechanisms incorporating anatomical prior knowledge to improve diagnostic accuracy and efficiency. These advancements hold significant potential for improving the speed and accuracy of thorax disease diagnosis, particularly in resource-constrained settings and for rare diseases, ultimately benefiting patient care and clinical decision-making.