[MICCAI2019]Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation

时间 2020-12-30

标签多模分割 MICCAI2019 繁體版

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MICCAI2019 多模分割相关论文笔记 Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation 基于 CNN 的多模 MR 图像分析通常会在一层或几层融合了多个下采样流，特征融合通常是通过简单的求和或串联进行的，而没有进行优化。作者提出了一种监督图像融合方法，以选择性地融合来自不同模态的有用

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1. 《MICCAI2019》Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
2. Deep Image Retrieval: Learning global representations for image search
3. [DIS]Deep Dual Learning for Semantic Image Segmentation
4. 【SegNet】 A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
5. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
6. Deep Convolutional Nets for Semantic Image Segmentation with Deep Gaussian CRFs
7. Weakly-supervised convolutional neural networks for multimodal image registration
8. Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
9. Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation文章解读
10. Deep Snake for Real-Time Instance Segmentation

>>更多相关文章<<