Contrastive Language-Image Pre-training (CLIP), a straightforward yet effective pre-training paradigm, successfully introduces semantic-rich text supervision to vision models and has demonstrated promising results in various tasks due to its generalizability and interpretability. It has recently gained increasing interest in the medical imaging domain, either as a powerful pre-training paradigm for medical vision language alignment or a pre-trained key component for various clinical tasks. With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP paradigm within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications. Our survey (1) starts with a brief introduction to the fundamentals of CLIP methodology. (2) Then, we investigate the adaptation of CLIP pre-training in the medical domain, focusing on how to optimize CLIP given characteristics of medical images and reports. (3) Furthermore, we explore the practical utilization of CLIP pre-trained models in various tasks, including classification, dense prediction, and cross-modal tasks. (4) Finally, we discuss existing limitations of CLIP in the context of medical imaging and propose forward-looking directions to address the demands of medical imaging domain. We expect that this comprehensive survey will provide researchers in the field of medical image analysis with a holistic understanding of the CLIP paradigm and its potential implications. The project page is available at https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging, which will be regularly updated.

翻译：对比语言-图像预训练（CLIP）是一种简洁而有效的预训练范式，通过引入语义丰富的文本监督成功赋能视觉模型，并凭借其泛化性与可解释性在多种任务中展现出显著潜力。近年来，CLIP在医学影像领域日益受到关注，既可作为医学视觉-语言对齐的强大预训练范式，也可作为各类临床任务中的预训练关键组件。为促进对这一前沿方向的深入理解，本综述对医学影像领域的CLIP范式进行了全面探索，涵盖精细化CLIP预训练与CLIP驱动的应用两大方面。具体而言：（1）首先简要介绍CLIP方法的基本原理；（2）继而探究CLIP预训练在医学领域的适配策略，聚焦如何根据医学图像与报告的特性优化CLIP；（3）进一步探讨CLIP预训练模型在分类、稠密预测及跨模态任务等实际应用中的使用方式；（4）最后讨论CLIP在医学影像场景下的现有局限，并提出面向医学影像领域需求的前瞻性方向。本综述旨在为医学图像分析领域的研究者提供CLIP范式及其潜在影响的系统性认知。项目页面（https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging）将定期更新。