Aligning Medical Images with General Knowledge from Large Language Models
This work addresses the challenge of aligning medical images with general knowledge for improved analysis, though it appears incremental as it builds on existing CLIP models with prompt learning.
The authors tackled the problem of adapting general vision-language models to medical image analysis by proposing ViP, a visual symptom-guided prompt learning framework that transfers knowledge from CLIP, achieving state-of-the-art performance on two challenging datasets.
Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and demonstrated promising generalization ability. In this work, we propose ViP, a novel visual symptom-guided prompt learning framework for medical image analysis, which facilitates general knowledge transfer from CLIP. ViP consists of two key components: a visual symptom generator (VSG) and a dual-prompt network. Specifically, VSG aims to extract explicable visual symptoms from pre-trained large language models, while the dual-prompt network utilizes these visual symptoms to guide the training on two learnable prompt modules, i.e., context prompt and merge prompt, which effectively adapts our framework to medical image analysis via large VLMs. Extensive experimental results demonstrate that ViP can outperform state-of-the-art methods on two challenging datasets.