SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image - uni-medical/SAM-Med3D.

Oct 23, 2023 · SAM-Med3D, a promptable segmentation model characterized by the fully learnable 3D structure, is trained on this dataset using a two-stage procedure.

Curated the most extensive volumetric medical dataset to date for training, boasting 131K 3D masks and 247 categories.

Jul 6, 2024 · We propose SAM-Med3D-MoE, a novel framework that seamlessly integrates task-specific finetuned models with the foundational model, creating a unified model.

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.

(1) SAM-Med3D-MoE is the first to introduce MoE techniques to adaptively merge the general knowledge from the foundational model and specific domain knowledge ...

Oct 7, 2024 · We anticipate that SAM-Med3D-MoE can serve as a new framework for adapting the foundation model to specific areas in medical image analysis.

We're on a journey to advance and democratize artificial intelligence through open source and open science.

Oct 29, 2023 · Our SAM-Med3D excels at capturing 3D spatial information, exhibiting competitive performance with significantly fewer prompt points than the top ...