Neural Netw. 2025 Jan 16;185:107168. doi: 10.1016/j.neunet.2025.107168. Online ahead of print.
ABSTRACT
Pre-training and fine-tuning have become popular due to the rich representations embedded in large pre-trained models, which can be leveraged for downstream medical tasks. However, existing methods typically either fine-tune all parameters or only task-specific layers of pre-trained models, overlooking the variability in input medical images. As a result, these approaches may lack efficiency or effectiveness. In this study, our goal is to explore parameter-efficient fine-tuning (PEFT) for medical image analysis. To address this challenge, we introduce a novel method called Dynamic Visual Prompt Tuning (DVPT). It can extract knowledge beneficial to downstream tasks from large models with only a few trainable parameters. First, the frozen features are transformed by a lightweight bottleneck layer to learn the domain-specific distribution of downstream medical tasks. Then, a few learnable visual prompts are employed as dynamic queries to conduct cross-attention with the transformed features, aiming to acquire sample-specific features. This DVPT module can be shared across different Transformer layers, further reducing the number of trainable parameters. We conduct extensive experiments with various pre-trained models on medical classification and segmentation tasks. We find that this PEFT method not only efficiently adapts pre-trained models to the medical domain but also enhances data efficiency with limited labeled data. For example, with only 0.5% additional trainable parameters, our method not only outperforms state-of-the-art PEFT methods but also surpasses full fine-tuning by more than 2.20% in Kappa score on the medical classification task. It can save up to 60% of labeled data and 99% of storage cost of ViT-B/16.
PMID:39827840 | DOI:10.1016/j.neunet.2025.107168