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...
图8:SAM-Med3D与性能最好的二维微调SAM模型SAM-Med2D在34个主要器官和5种病变上的Dice系数比较。∗和∗∗分别代表可见病灶和未见病灶。 迁移性评估 作者将 SAM-Med3D 预训练的 ViT 图像编码器迁移到 UNETR 中进行使用,发现能够获得效果上的提升,证明了作者提出的 SAM-Med3D 具有迁移能力,这将能够对三维医...
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 ...
SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image - SAM-Med3D/val.sh at main · uni-medical/SAM-Med3D
7 lines (7 loc) · 204 Bytes Raw python inference.py --seed 2024\ -cp ./ckpt/sam_med3d_turbo.pth \ -tdp ./data/medical_preprocessed -nc 1 \ --output_dir ./results \ --task_name infer_turbo #--sliding_window #--save_image_and_gt...
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-...