SegAN:Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation-筆記
這次介紹將對抗網絡第一次應用到醫學圖像分割的文章。 代碼 作者提出用對抗網絡進行分割，損失函數使用多尺度L1損失函數。該論文的判別器和生成器分別爲critic network (C)和segmentor network (S)。 首先介紹一下SegAN與原始的GAN的區別。 1，損失函數的區別。經典的GAN的generator和discriminator的損失是分開的。本論文爲segmentor和
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相關文章
- 1. [論文筆記] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- 2. CE-Net: Context encoder network for 2D medical image segmentation
- 3. Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
- 4. Structure Boundary Preserving Segmentation for Medical Image with Ambiguous Boundary(CVPR 2020)論文...
- 5. 論文閱讀筆記（五十四）：V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 6. Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
- 7. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation-論文筆記
- 8. 【閱讀筆記】《Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network》（JSIS-Net）
- 9. Adversarial learning with multi-scale loss for skin lesion segmentation
- 10. Attention Guided Network for Retinal Image Segmentation