Medical Image Representation

Medical image representation research focuses on developing efficient and accurate methods to encode and utilize information from medical images, primarily to improve diagnostic accuracy and efficiency. Current efforts concentrate on multimodal approaches, integrating textual data (e.g., medical reports) with visual data through contrastive learning and transformer-based architectures, often employing pre-training strategies to mitigate data scarcity. These advancements hold significant promise for improving various downstream medical image tasks, such as classification, segmentation, and report generation, ultimately leading to better patient care and more efficient workflows.