Chest X Ray Datasets

Chest X-ray datasets are crucial for developing and evaluating artificial intelligence models for diagnosing thoracic diseases, with current research focusing on addressing challenges like class imbalance, data scarcity, and algorithmic bias. Researchers are exploring various deep learning architectures, including convolutional neural networks (CNNs), vision transformers (ViTs), and Siamese networks, often incorporating techniques like few-shot learning and self-supervised learning to improve model performance and generalizability. The accurate and unbiased analysis of these datasets is vital for improving the reliability and fairness of AI-driven diagnostic tools, ultimately impacting patient care and clinical workflows.