开源代码:github.com/uni-medical/ 二维模型很难扩展到三维,三维通用模型缺失 最近提出的视觉基础模型 "Segment Anything Model"(SAM)是一种使用超过 10 亿个掩码(mask)进行训练的 ViT 模型,它在多个领域都展现出出色的零样本分割性能。 SAM的兴起可以推动数据标注快速迭代,并为3D 医学图像分析的发展提供新的机遇...
SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image - SAM-Med3D/val.sh at main · uni-medical/SAM-Med3D
SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image - SAM-Med3D/infer.sh at main · uni-medical/SAM-Med3D
enhanced efficiency and broad segmentation capabilities for 3D volumetric medical images. Our code is released athttps://github.com/uni-medical/SAM-Med3D. 尽管“分割任何模型”(SAM)在2D自然图像分割方面展示了令人印象深刻的性能,但其应用于3D体积医学图像时却显示出明显的不足,主要表现为次优的性能和不稳...
0.8440 Dice score while using only 33.87% of SAM-Med3D parameters. It also accelerates inference by3.36×over SAM-Med3D. Thus, TinySAM-Med3D facilitates deploying SAMs for fast 3D medical image segmentation. Our code and models are available athttps://github.com/songty21110133/TinySAM-Med3D...
It also accelerates inference by [Math Processing Error] 3.36 × 3.36{imes } over SAM-Med3D. Thus, TinySAM-Med3D facilitates deploying SAMs for fast 3D medical image segmentation. Our code and models are available at https://github.com/songty21110133/TinySAM-Med3D . 展开 ...
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,欢迎加入群聊与我们一起探讨!(二维码见文末) 论文:https://arxiv.org/abs/2310.15161 开源代码:https://github.com/uni-medical/sam-med3d...
Our code and models are available at https://github.com/songty21110133/TinySAM-Med3D .Song, TianyuanBeijing University of Posts and TelecommunicationsKang, GuixiaBeijing University of Posts and TelecommunicationsShen, YiqingJohns Hopkins University...
Title题目SAM-Med3D01文献速递介绍医学图像分析已成为现代医疗保健不可或缺的基石,辅助诊断、治疗计划和进一步的医学研究]。在这一领域中最重要的挑战之一是精确分割体积医学图像。尽管众多方法在一系列目标上展现了值得称赞的有效性,但现有的分割技术倾向于专门针对特定器官或病变。这种倾向是由体积医学图像的固有特性所...
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