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English(EN) MNet++: Extended 2D/3D Networks for Anisotropic Medical Image Segmentation

MNet++ 通过自适应融合和状态空间建模增强医学图像分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员成功复现并扩展了 MNet，这是一个用于医学图像分割的混合二维/三维卷积网络。该研究在对前列腺 MRI 和肝脏 CT 数据集进行了验证，取得了较高的 Dice 系数。引入了两个新扩展：一个用于自适应特征融合的学习式门控机制（Fusion Gating）和一个用于改进长程建模的 VMamba 模块，两者都保持了对各向异性条件的鲁棒性。 AI

排序理由 该集群包含一篇详细介绍新模型扩展和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

MNet++ 通过自适应融合和状态空间建模增强医学图像分割

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该集群包含一篇详细介绍新模型扩展和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kirsten Odendaal, Rade Bajic ·

    MNet++：用于各向异性医学图像分割的扩展二维/三维网络

    arXiv:2606.15370v1 Announce Type: cross Abstract: This work demonstrates a full reproduction and extension of MNet, a hybrid 2D/3D convolutional network designed for anisotropic medical image segmentation. The original architecture was re-implemented within the nnU-Net framework …

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