MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis

• URL: http://arxiv.org/abs/2311.08236v2
• Date: Mon, 22 Jul 2024 05:39:53 GMT
• Title: MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis
• Authors: Yitao Zhu, Zhenrong Shen, Zihao Zhao, Sheng Wang, Xin Wang, Xiangyu Zhao, Dinggang Shen, Qian Wang,
• Abstract summary: Vision Transformers (ViT) have become much larger and less accessible to medical imaging communities. MeLo (Medical image Low-rank adaptation) adopts low-rank adaptation instead of resource-demanding fine-tuning. Our proposed method achieves comparable performance to fully fine-tuned ViT models on four distinct medical imaging datasets.
• Score: 63.59184480010552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The common practice in developing computer-aided diagnosis (CAD) models based on transformer architectures usually involves fine-tuning from ImageNet pre-trained weights. However, with recent advances in large-scale pre-training and the practice of scaling laws, Vision Transformers (ViT) have become much larger and less accessible to medical imaging communities. Additionally, in real-world scenarios, the deployments of multiple CAD models can be troublesome due to problems such as limited storage space and time-consuming model switching. To address these challenges, we propose a new method MeLo (Medical image Low-rank adaptation), which enables the development of a single CAD model for multiple clinical tasks in a lightweight manner. It adopts low-rank adaptation instead of resource-demanding fine-tuning. By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters. Specifically, our proposed method achieves comparable performance to fully fine-tuned ViT models on four distinct medical imaging datasets using about 0.17% trainable parameters. Moreover, MeLo adds only about 0.5MB of storage space and allows for extremely fast model switching in deployment and inference. Our source code and pre-trained weights are available on our website (https://absterzhu.github.io/melo.github.io/).

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