Related papers: Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation

Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation

URL: http://arxiv.org/abs/2304.10880v4
Date: Tue, 28 May 2024 07:28:44 GMT
Title: Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation
Authors: Jiachen Shen, Wenxuan Wang, Chen Chen, Jianbo Jiao, Jing Liu, Yan Zhang, Shanshan Song, Jiangyun Li,
Abstract summary: We introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task. Our framework enhances the 2D baselines's precision on segmentation tasks, which are pre-trained on natural images. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4x, with even better segmentation performance.
Score: 37.42382366505377
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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/}.

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