CLIP在医学图像多模态分析中的研究进展OA

Medical images have important clinical value,but their intelligent analysis methods still face challenges such as label scarcity,cross-modal disparity,and limited model generalization.In recent years,contrastive language-image pre-training(CLIP)shows potential for cross-modal alignment,label-efficient learning,and task transfer.This paper surveys commonly used medical image-text datasets,analyzes CLIP architectures and cross-modal properties,and summarizes applications in image classification,retrieval,segmentation,and medical visual question answering.In combination with engineering adaptability and clinical deployment constraints,the advantages and disadvantages of different methods in terms of cross-center generalization,semantic alignment accuracy,and inference efficiency are evaluated.Finally,the future research directions such as dynamic prompt construction,structured information modeling,and model lightweighting are discussed,providing references for the clinical deployment of multimodal medical artificial intelligence models.