CLIP in medical imaging: A survey

doi:10.1016/j.media.2025.103551

	CLIP in medical imaging: A survey
	Zhao, Zihao1,2 ; Liu, Yuxiao1,2 ; Wua, Han1,2 ; Wang, Mei 1,2,3; Li, Yonghao1,2 ; Wang, Sheng 1,2,4; Teng, Lin1,2 ; Liu, Disheng 1,2; Cui, Zhiming1,2 ; Wang, Qian1,2 ; Shen, Dinggang1,2,5,6
	2025-05-01
发表期刊	MEDICAL IMAGE ANALYSIS (IF:10.7[JCR-2023],11.9[5-Year])
ISSN	1361-8415
EISSN	1361-8423
卷号	102
发表状态	已发表
DOI	10.1016/j.media.2025.103551
摘要	Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models. It has shown promising results across various tasks due to its generalizability and interpretability. The use of CLIP has recently gained increasing interest in the medical imaging domain, serving as a pre-training paradigm for image-text alignment, or a critical component in diverse clinical tasks. With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications. In this paper, we (1) first start with a brief introduction to the fundamentals of CLIP methodology; (2) then investigate the adaptation of CLIP pre-training in the medical imaging domain, focusing on how to optimize CLIP given characteristics of medical images and reports; (3) further explore practical utilization of CLIP pre-trained models in various tasks, including classification, dense prediction, and cross-modal tasks; and (4) finally discuss existing limitations of CLIP in the context of medical imaging, and propose forward-looking directions to address the demands of medical imaging domain. Studies featuring technical and practical value are both investigated. We expect this survey will provide researchers with a holistic understanding of the CLIP paradigm and its potential implications. The project page of this survey can also be found on Github.
关键词	Medical image analysis Image-text alignment Vision language model Constrative languagr-image pre training
URL	查看原文
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China["U23A20295","82441023","62131015","82394432"] ; China Ministry of Science and Technology["S20240085","2022ZD0209000","2022ZD0213100"] ; Shanghai Municipal Central Guided Local Science and Technology Development Fund[YDZX20233100001001]
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001454956200001
出版者	ELSEVIER
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/514082
专题	生物医学工程学院信息科学与技术学院_硕士生信息科学与技术学院_博士生生物医学工程学院_PI研究组_沈定刚组生物医学工程学院_PI研究组_王乾组生物医学工程学院_PI研究组_崔智铭组生物医学工程学院_博士生
通讯作者	Cui, Zhiming; Wang, Qian; Shen, Dinggang
作者单位	1.ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China 2.ShanghaiTech Univ, State Key Lab Adv Med Mat & Devices, Shanghai, Peoples R China 3.Southern Med Univ, Sch Biomed Engn, Guangzhou, Peoples R China 4.Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China 5.Shanghai United Imaging Intelligence Co Ltd, Dept Res & Dev, Shanghai, Peoples R China 6.Shanghai Clin Res & Trial Ctr, Shanghai, Peoples R China
第一作者单位	生物医学工程学院; 上海科技大学
通讯作者单位	生物医学工程学院; 上海科技大学
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Zhao, Zihao,Liu, Yuxiao,Wua, Han,et al. CLIP in medical imaging: A survey[J]. MEDICAL IMAGE ANALYSIS,2025,102.
APA	Zhao, Zihao.,Liu, Yuxiao.,Wua, Han.,Wang, Mei.,Li, Yonghao.,...&Shen, Dinggang.(2025).CLIP in medical imaging: A survey.MEDICAL IMAGE ANALYSIS,102.
MLA	Zhao, Zihao,et al."CLIP in medical imaging: A survey".MEDICAL IMAGE ANALYSIS 102(2025).