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Xiong F, Lu H, Li L, Jiang R.
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Pan S, Xiang X, Yan Q, Ding Y.
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Chen L, Wang X, Zhu K, Ren K, Wu Z.
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3DGE-UNet磁共振成像的脑胶质瘤全自动分割算法:对不充分全局特征提取的改进
Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition目的 脑胶质瘤及其子区域的全自动分割是计算机辅助肿瘤临床诊断的基础,卷积神经网络在脑部磁共振成像(magnetic resonance imaging, MRI)分割过程中,小卷积核只能提取局部特征而无法有效融合全局特征,缩小了对影像信息的感知范围,从而导致分割精度不足的问题。本研究旨在利用膨胀卷积,针对三维(three-dimensional, 3D)-UNet不能充分提取全局特征的问题进行改进。 方法 ①算法构建:本文提出一种带有三条全局上下文特征提取通路的3D-UNet模型,即3DGE ...
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