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.

翻译：参数高效微调（PEFT）最初是为利用预训练大语言模型而开发，近期已成为在计算机视觉任务中实现迁移学习的有效方法。然而，PEFT在医学视觉基础模型上的有效性仍不明确，有待探索。作为概念验证，我们对胸部X光片基础模型应用PEFT进行了详细的实证研究。具体而言，我们深入研究了代表性PEFT方法LoRA，并将其与全参数微调（FFT）在两个自监督X光片基础模型上进行了比较，涉及三个成熟的胸部X光片数据集。结果显示，在18个迁移学习任务中，LoRA在13个任务上以不到1%的可调参数超越了FFT，性能最高提升2.9%。将LoRA与基础模型结合，我们在多项数据高效学习任务上实现了新的最优结果，例如在NIH ChestX-ray14数据集上使用1%标注数据取得了80.6%的AUROC评分。我们希望本研究能引发学界更多关注PEFT在医学影像迁移学习中的应用。代码和模型已开源：https://github.com/RL4M/MED-PEFT。