Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation
This work addresses efficiency challenges for researchers and practitioners in medical imaging by providing an incremental improvement over existing parameter-efficient tuning methods.
The paper tackles the high computational and memory costs of full fine-tuning for medical volumetric segmentation by introducing Med-Tuning, a parameter-efficient tuning framework that reduces fine-tuned parameters by up to 4x while achieving better segmentation performance than full fine-tuning on benchmark datasets.
The "pre-training then fine-tuning (FT)" paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines's precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4x, with even better segmentation performance. Our project webpage is at \url{https://rubics-xuan.github.io/Med-Tuning/}.