SAM-Med3D: 进一步加速数据和模型的生产与迭代 医学图像和自然图像之间存在显著的差异,而且医学图像领域缺乏大规模的基准数据集,这是导致AI在医学领域进展缓慢的重要原因之一。通过构建大规模的基准数据集和可靠的基线模型,我们可以推动AI在医疗领域的快速发展,并加速医疗向更通用方向转变的进程。如果您对此话题感兴趣,欢...
Baseline 为 SAM-Med3D 微调前的性能,可以看到在新的下游任务上表现不够好,因为训练数据中没有对应分布的图像;FT-expert 为仅仅在 SPPIN2023 上微调 Mask Decoder的性能表现,可以看出虽然在 SPPIN 上效果较为优秀,但是在之前的任务上性能降低很多;作者提出的 SAM-Med3D-MoE 可以看出,在之前的任务上性能几乎没有...
此外,作者还验证了 SAM-Med3D 的迁移能力:将其编码器用作预训练模型,在多个全监督分割任务中进行了验证。 综合全面的评估结果,SAM-Med3D 具有以下两个主要优势: 更高的效率:SAM-Med3D 的性能与在二维上微调的 SAM 相比更具竞争力,只需要更少的提示点便能达到更好的效果。与二维模型需要在每个切片上交互相比,...
encompassing a 3D image encoder, 3D prompt encoder, and 3D mask decoder. 3D convolution, 3D positional encoding (PE) and 3D layer norm are employed to construct the 3D model.图3:我们的SAM-Med3D修改后的3D架构。原始
While existing volumetric foundation modelsfor medical image segmentation, such as SAM-Med3D and SegVol, haveshown 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 ...
SAM by fine-tuning the model. However, the encoders are based on the ViT architecture, which incurs high computational costs when applied directly to 3D medical data, limiting real-time performance on resource-constrained devices. To address these limitations, we introduce TinySAM-Med3D, an ...
We anticipatethat SAM-Med3D-MoE can serve as a new framework for adaptingthe foundation model to specific areas in medical image analysis.Codes and datasets will be publicly available. 展开 会议名称: International Conference on Medical Image Computing and Computer-Assisted Intervention ...
Restart 3D Slicer. Upgrade Remove all pre-existing files from step#2 and install the new version as instructed before. Usage You can watch a video guide for usagehere. From theWelcome to Slicerdrop-down menu, under theSegmentationsub-menu,MedSAMLiteoption is added. By choosing it, you get...
SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image - SAM-Med3D/val.sh at main · uni-medical/SAM-Med3D
TinySAM-Med3D builds on SAM-Med3D by distilling the encoder to a lightweight TinyViT and substituting the multilayer perceptron with a Mixture of Experts (MoEs) to preserve performance while significantly reducing computational and memory costs. This enables real-time segmentation on resource-...