Medical Image Recognition

Medical image recognition uses computer vision to analyze medical images (e.g., X-rays, MRIs, videos) for diagnosis and treatment planning, aiming to improve accuracy, efficiency, and accessibility of healthcare. Current research emphasizes leveraging large pre-trained models, including vision transformers and convolutional neural networks, often combined with self-supervised or weakly supervised learning techniques to address data scarcity and annotation costs. These advancements are improving the performance of various tasks, such as disease detection, organ segmentation, and image registration, with significant potential to enhance clinical workflows and patient care.