Medical Knowledge Intervention Prompt Tuning for Medical Image Classification

TL;DR

This paper introduces CILMP, a novel prompt tuning method that leverages large language models to incorporate disease-specific medical knowledge into vision-language models, significantly improving medical image classification performance.

Contribution

CILMP is the first approach to integrate LLM-derived medical knowledge into prompt tuning for VLMs, enabling instance-adaptive prompts for better medical image classification.

Findings

CILMP outperforms existing prompt tuning methods across multiple datasets.

The method effectively captures disease-specific features in prompts.

Conditional intervention improves prompt adaptability to individual images.

Abstract

Vision-language foundation models (VLMs) have shown great potential in feature transfer and generalization across a wide spectrum of medical-related downstream tasks. However, fine-tuning these models is resource-intensive due to their large number of parameters. Prompt tuning has emerged as a viable solution to mitigate memory usage and reduce training time while maintaining competitive performance. Nevertheless, the challenge is that existing prompt tuning methods cannot precisely distinguish different kinds of medical concepts, which miss essentially specific disease-related features across various medical imaging modalities in medical image classification tasks. We find that Large Language Models (LLMs), trained on extensive text corpora, are particularly adept at providing this specialized medical knowledge. Motivated by this, we propose incorporating LLMs into the prompt tuning process. Specifically, we introduce the CILMP, Conditional Intervention of Large Language Models for Prompt Tuning, a method that bridges LLMs and VLMs to facilitate the transfer of medical knowledge into VLM prompts. CILMP extracts disease-specific representations from LLMs, intervenes within a low-rank linear subspace, and utilizes them to create disease-specific prompts. Additionally, a conditional mechanism is incorporated to condition the intervention process on each individual medical image, generating instance-adaptive prompts and thus enhancing adaptability. Extensive experiments across diverse medical image datasets demonstrate that CILMP consistently outperforms state-of-the-art prompt tuning methods, demonstrating its effectiveness. Code is available at https://github.com/usr922/cilmp.