Efficient Medical Image Analysis with CLIP: A New Approach Using Text and Visual Prompts

What is it about?

In this paper, we present a new method, called TVTL-CLIP, for analyzing medical images, specifically images from capsule endoscopy. Traditional methods of training models for medical image analysis often require large amounts of labeled data and computational resources, but our method significantly reduces these needs. By using a frozen CLIP model and introducing small sets of learnable text and visual prompts, we adapt the model to recognize different types of gastrointestinal lesions without updating its underlying parameters. The results show that our approach achieves high accuracy, recall, and specificity while only requiring a small fraction of the resources compared to traditional models, making it a promising solution for real-world clinical applications with limited data.

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Photo by Vitaly Gariev on Unsplash

Photo by Vitaly Gariev on Unsplash

Why is it important?

This work is important because it offers a more efficient approach to medical image analysis, especially in fields like gastrointestinal pathology, where annotated data can be scarce. By leveraging the power of large, pretrained models like CLIP and using a minimal number of parameters for adaptation, we make it possible to develop accurate medical image classifiers with fewer resources. This method could be crucial for deploying AI-based diagnostic tools in clinical settings with limited data and computational power, improving healthcare accessibility and efficiency.

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