MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation

arXiv:2403.20253v1 Announce Type: cross
Abstract: Medical image segmentation of anatomical structures and pathology is crucial in modern clinical diagnosis, disease study, and treatment planning. To date, great progress has been made in deep learning-based segmentation techniques, but most methods still lack data efficiency, generalizability, and interactability. Consequently, the development of new, precise segmentation methods that demand fewer labeled datasets is of utmost importance in medical image analysis. Recently, the emergence of foundation models, such as CLIP and Segment-Anything-Model (SAM), with …

abstract arxiv clinical cs.cv cs.lg data deep learning development diagnosis disease efficiency image medical modern pathology planning progress sam segmentation study text treatment type universal