VinDr CXR

VinDr CXR and VinDr-Mammo are large, publicly available datasets of chest X-rays and mammograms, respectively, serving as benchmarks for developing and evaluating computer-aided diagnosis (CAD) systems. Current research focuses on improving the accuracy and generalizability of these systems across diverse patient populations and image qualities, employing techniques like transfer learning, self-supervised learning (e.g., Barlow Twins), vision-language models (e.g., LiteGPT), and multi-task learning to address challenges such as domain shift and low-resolution images. These advancements hold significant potential for improving the efficiency and accuracy of medical image interpretation, ultimately aiding radiologists in earlier and more accurate disease detection and potentially reducing healthcare disparities.