Medical Image Retrieval

Medical image retrieval aims to efficiently locate similar images within large medical databases, aiding diagnosis, treatment planning, and medical education. Current research heavily emphasizes leveraging pre-trained convolutional neural networks (CNNs) and, increasingly, foundation models like CLIP variants, as feature extractors for improved retrieval accuracy, particularly for 2D images; techniques like contrastive learning and triplet loss functions are also being refined to enhance performance. This field is crucial for advancing medical research and improving healthcare by enabling faster access to relevant case studies and facilitating large-scale analyses of medical images.