Fusion Side Tuning: A Parameter and Memory Efficient Fine-tuning Method for High-resolution Medical Image Classification

Parameter-efficient fine-tuning (PEFT) has been proposed as a cost-effective approach for transferring large-scale pre-trained models (LPMs) to downstream tasks, mitigating the high costs associated with updating all parameters of LPMs. However, current PEFT methods encounter the challenge that GPU memory usage during training is not reduced as effectively as parameter usage. In this paper, we propose Fusion Side Tuning (FST), a novel memory-efficient and parameter-efficient fine-tuning method. FST significantly reduces GPU memory consumption during training, particularly at high resolutions. To achieve this, we freeze the backbone LPM and construct a learnable side fusion network that takes intermediate features from the backbone as input. The side fusion network consists of a sequence of fusion modules, which enable it to leverage the knowledge embedded in the intermediate features. Additionally, we employ an important token selection mechanism to further reduce training costs and memory requirements. We evaluate FST on eight medical image datasets of varying modalities and sizes. Experimental results demonstrate that FST outperforms existing PEFT methods, utilizing only 2% of the learnable parameters and 20% of the GPU memory required for full fine-tuning of a ViT-B encoder with an input resolution of 512 × 512.