SegAN:Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation-笔记
这次介绍将对抗网络第一次应用到医学图像分割的文章。 代码 作者提出用对抗网络进行分割，损失函数使用多尺度L1损失函数。该论文的判别器和生成器分别为critic network (C)和segmentor network (S)。 首先介绍一下SegAN与原始的GAN的区别。 1，损失函数的区别。经典的GAN的generator和discriminator的损失是分开的。本论文为segmentor和
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相关文章
- 1. [论文笔记] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
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