Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation文章解读
本文针对全卷积神经网络有大量的参数，同时伴随着需要大量的训练数据集，然而由于医学图像的特殊性，如此大量的有标签数据不好提供，因此本文的贡献点之一是缓解了数据集少的问题。在公式2的目标函数中，通过引入右边第二项来定义这个半监督的优化问题；对于网络优化，这是本文的精髓所在： a.固定网络参数，采用此时的网络去评估未知数据的标签 b.第二步，未知数据的标签采用第一步评估的结果固定，然后采用已标注
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相关文章
- 1. [MICCAI2019]Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images
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- 3. 《MICCAI2019》Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
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- 6. 【论文阅读】Learning Deconvolution Network for Semantic Segmentation
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- 9. 文章阅读：Multi-scale guided attention for medical image segmentation
- 10. Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation