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CLIP in medical imaging: A survey
Zihao Zhao1, Yuxiao Liu1, Han Wu1
1School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
Medical Image Analysis
|March 24, 2025
Summary
This survey explores Contrastive Language-Image Pre-training (CLIP) in medical imaging, detailing its adaptation, applications, and future directions for enhanced clinical tasks.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Computer Vision
Background:
- Contrastive Language-Image Pre-training (CLIP) offers text supervision for vision models, demonstrating generalizability and interpretability.
- CLIP is increasingly adopted in medical imaging for pre-training and clinical applications.
Purpose of the Study:
- To provide a comprehensive overview of CLIP in medical imaging.
- To explore CLIP pre-training adaptations and CLIP-driven applications in clinical settings.
Main Methods:
- Introduction to CLIP fundamentals.
- Investigation of CLIP pre-training optimization for medical data.
- Exploration of CLIP model applications in classification, dense prediction, and cross-modal tasks.
- Discussion of limitations and future research directions.
Main Results:
- CLIP shows promise for image-text alignment in medical contexts.
- Adapted CLIP models can be utilized across various clinical tasks.
- Identified limitations and potential solutions for CLIP in medical imaging.
Conclusions:
- CLIP presents a valuable paradigm for medical imaging research and applications.
- Further research is needed to address existing limitations and fully leverage CLIP's potential in healthcare.
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