Work

Aligning Medical Images with General Knowledge from Large Language Models

Year: 2024

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2409.00341

Authors: Fang, Xiao, Lin, Yi, Zhang, Dong, Cheng, Kwang-Ting, Chen, Hao

Institutions: Hong Kong University of Science and Technology, Hong Kong College of Technology

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: Innovation and Technology Fund