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, attributable to its generalizability and interpretability. The use of CLIP has recently gained increasing interest in the medical imaging domain, serving both as a pre-training paradigm for aligning medical vision and language, and as 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 paradigm within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications. In this study, We (1) start 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 can be found on https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging.

翻译：对比语言-图像预训练（CLIP）作为一种简单而有效的预训练范式，成功将文本监督引入视觉模型。因其良好的泛化性与可解释性，CLIP已在多种任务中展现出显著成效。近年来，CLIP在医学影像领域受到日益广泛的关注，既用于对齐医学视觉与语言的预训练范式，也作为多类临床任务的关键组成部分。为促进对该方向的深入理解，本综述系统探讨了CLIP范式在医学影像领域的发展，涵盖精细化CLIP预训练与CLIP驱动的应用。具体而言，我们（1）首先简要介绍CLIP方法论的基础原理；（2）随后探究CLIP预训练在医学领域的适配策略，聚焦如何基于医学图像与报告的特征优化CLIP；（3）进而剖析CLIP预训练模型在分类、密集预测及跨模态任务中的实际应用；（4）最后讨论CLIP在医学影像场景下的现有局限，并针对医学影像领域的特殊需求提出前瞻性发展方向。我们期望本综述能为医学图像分析领域的研究者提供对CLIP范式及其潜在影响的系统性认知。项目页面详见https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging。