bibtext: @InProceedings{ Wan_SAMMed3DMoE_MICCAI2024,
                   author = { Wang, Guoan and Ye, Jin and Cheng, Junlong and Li, Tianbin and Chen, Zhaolin and Cai, Jianfei and He, Junjun and Zhuang, Bohan },
                   title = { { SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: Volumetric medical image segmentation is pivotal in enhancing disease diagnosis, treatment planning, and advancing medical research. While existing volumetric foundation models for medical image segmentation, such as SAM-Med3D and SegVol, have shown remarkable performance on general organs and tumors, their ability to segment certain categories in clinical downstream tasks remains limited. Supervised Finetuning (SFT) serves as an effective way to adapt such foundation models for task-specific downstream tasks and achieve remarkable performance in those tasks. However, it would inadvertently degrade the general knowledge previously stored in the original foundation model. In this paper, we propose SAM-Med3D-MoE, a novel framework that seamlessly integrates task-specific finetuned models with the foundational model, creating a unified model at minimal additional training expense for an extra gating network. This gating network, in conjunction with a selection strategy, allows the unified model to achieve comparable performance of the original models in their respective tasks — both general and specialized — without updating any parameters of them. Our comprehensive experiments demonstrate the efficacy of SAM-Med3D-MoE, with an average Dice performance increase from 53.2\% to 56.4\% on 15 specific classes. It especially gets remarkable gains of 29.6\%, 8.5\%, 11.2\% on the spinal cord, esophagus, and right hip, respectively. Additionally, it achieves 48.9\% Dice on the challenging SPPIN2023 Challenge, significantly surpassing the general expert’s performance of 32.3\%. We anticipate that SAM-Med3D-MoE can serve as a new framework for adapting the foundation model to specific areas in medical image analysis. Codes and datasets will be publicly available.
title: SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
authors: Wang, Guoan Ye, Jin Cheng, Junlong Li, Tianbin Chen, Zhaolin Cai, Jianfei He, Junjun Zhuang, Bohan