SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via
Mixture of Experts for 3D Medical Image Segmentation

SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation

6 July 2024

Jin Ye

Tianbin Li

Zhaolin Chen

Jianfei Cai

Papers citing "SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation"

6 / 6 papers shown

Title
Mixtral of Experts Albert Q. Jiang Alexandre Sablayrolles Antoine Roux A. Mensch Blanche Savary ... Théophile Gervet Thibaut Lavril Thomas Wang Timothée Lacroix William El Sayed MoE LLMAG 98 1,049 0 08 Jan 2024
LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin Shihan Dou Enyu Zhou Yan Liu Songyang Gao Jun Zhao ... Jiang Zhu Rui Zheng Tao Gui Qi Zhang Xuanjing Huang CLL MoE KELM 37 32 0 15 Dec 2023
SegVol: Universal and Interactive Volumetric Medical Image Segmentation Yuxin Du Fan Bai Tiejun Huang Bo Zhao VLM 70 40 0 22 Nov 2023
LoRA: Low-Rank Adaptation of Large Language Models J. E. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang Weizhu Chen OffRL AI4TS AI4CE ALM AIMat 273 10,099 0 17 Jun 2021
The Medical Segmentation Decathlon Michela Antonelli Annika Reinke Spyridon Bakas Keyvan Farahani AnnetteKopp-Schneider ... Zhanwei Xu Yefeng Zheng Amber L. Simpson Lena Maier-Hein M. Jorge Cardoso OOD 88 974 0 10 Jun 2021
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.3K 76,547 0 18 May 2015

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