CE-Net: Context encoder network for 2D medical image segmentation
CE-Net: Context encoder network for 2D medical image segmentation Abstract: 医学图像分割是医学图像分析中的重要步骤。随着卷积神经网络在图像处理中的快速发展，深度学习已被用于医学图像分割，如视盘分割，血管检测，肺部分割，细胞分割等。以前，已经提出了基于U-net的方法。 。然而，连续的汇集和跨步卷积操作导致一些空间信息的丢失
相关文章
- 1. [论文笔记] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- 2. 3D图像分割：Thickened 2D Networks for 3D Medical Image Segmentation
- 3. Context-Aware U-Net for Biomedical Image Segmentation
- 4. Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
- 5. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 6. SegAN:Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation-笔记
- 7. 【SegNet】 A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 8. [论文翻译]UNet++: A Nested U-Net Architecture for Medical Image Segmentation
- 9. Attention Guided Network for Retinal Image Segmentation
- 10. Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
- 更多相关文章...
- • RSS
元素 - RSS 教程 - • ASP.NET Image 控件 - ASP.NET 教程
- • Tomcat学习笔记（史上最全tomcat学习笔记）
- • Flink 数据传输及反压详解
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. [论文笔记] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- 2. 3D图像分割：Thickened 2D Networks for 3D Medical Image Segmentation
- 3. Context-Aware U-Net for Biomedical Image Segmentation
- 4. Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
- 5. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 6. SegAN:Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation-笔记
- 7. 【SegNet】 A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 8. [论文翻译]UNet++: A Nested U-Net Architecture for Medical Image Segmentation
- 9. Attention Guided Network for Retinal Image Segmentation
- 10. Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation