Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models

TL;DR

This paper demonstrates that parameter-efficient fine-tuning methods like LoRA can outperform full-parameter fine-tuning on medical vision models, achieving state-of-the-art results with fewer tunable parameters and less labeled data.

Contribution

The study provides the first comprehensive empirical evaluation of PEFT methods on medical vision foundation models, highlighting their effectiveness and efficiency.

Findings

LoRA outperforms FFT in 13/18 tasks by up to 2.9%

LoRA uses less than 1% of parameters for tuning

Achieved new state-of-the-art with 1% labeled data on NIH ChestX-ray14

Abstract

Parameter-efficient fine-tuning (PEFT) that was initially developed for exploiting pre-trained large language models has recently emerged as an effective approach to perform transfer learning on computer vision tasks. However, the effectiveness of PEFT on medical vision foundation models is still unclear and remains to be explored. As a proof of concept, we conducted a detailed empirical study on applying PEFT to chest radiography foundation models. Specifically, we delved into LoRA, a representative PEFT method, and compared it against full-parameter fine-tuning (FFT) on two self-supervised radiography foundation models across three well-established chest radiograph datasets. Our results showed that LoRA outperformed FFT in 13 out of 18 transfer learning tasks by at most 2.9% using fewer than 1% tunable parameters. Combining LoRA with foundation models, we set up new state-of-the-art on a range of data-efficient learning tasks, such as an AUROC score of 80.6% using 1% labeled data on NIH ChestX-ray14. We hope this study can evoke more attention from the community in the use of PEFT for transfer learning on medical imaging tasks. Code and models are available at https://github.com/RL4M/MED-PEFT.