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English(EN) BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation

BiSegMamba 提高了3D 医学图像分割的效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 BiSegMamba，一种用于3D医学图像分割的新型网络架构，可提高效率和准确性。与之前的基于 Mamba 的方法不同，BiSegMamba 采用双向三向方法从多个正交视图对长距离依赖性进行建模，显著降低了计算成本。在各种数据集上的实验表明，它在不同分割任务中的有效性，同时在效率方面优于现有模型。 AI

影响 引入了一种更高效、更准确的3D医学图像分割架构，有望提高诊断能力。

排序理由 该集群包含一篇详细介绍特定任务新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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BiSegMamba 提高了3D 医学图像分割的效率

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Bakht Zada, Chao Tong, Qile Su, Shuai Zhang ·

    BiSegMamba：用于3D医学图像分割的高效双向三向Mamba

    arXiv:2605.30972v1 Announce Type: new Abstract: Accurate 3D medical image segmentation requires both long-range volumetric context and fine boundary preservation. CNN-based methods have limited global dependency modeling, while Transformer-based models are often computationally e…

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