CXR Imaging

Chest X-ray (CXR) imaging analysis is rapidly advancing, driven by the need for accurate and efficient disease detection, particularly in resource-constrained settings. Current research focuses on improving the accuracy and reliability of deep learning models for CXR interpretation, addressing challenges like data bias, low-resolution images, and the need for explainable AI. This involves developing novel architectures such as vision transformers and employing techniques like knowledge distillation, multi-task learning, and style randomization to enhance model robustness and generalizability across diverse datasets and imaging conditions. These improvements hold significant potential for assisting radiologists, improving diagnostic accuracy, and ultimately enhancing patient care.