DEFORM-GAN:AN UNSUPERVISED LEARNING MODEL FOR DEFORMABLE REGISTRATION
https://arxiv.org/pdf/2002.11430.pdf 多模配準最困難的地方： 1、多模態或序列的similarity measurement，例如mutual information(MI)，需要binning or quantizing，這樣離散化會導致gradient vanishing， 2、no ground-truth . 在不同強度、噪聲、模糊的數據間進行多模配準。
相關文章
- 1. 【論文閱讀筆記】An Unsupervised Learning Model for Deformable Medical Image Registration
- 2. Multi-Modal Image Registration with Unsupervised Deep Learning
- 3. An Unsupervised Autoregressive Model for Speech Representation Learning
- 4. Supervised learning and Unsupervised learning
- 5. Clustering - Unsupervised learning introduction
- 6. Unsupervised Learning: Neighbor Embedding
- 7. Unsupervised Learning-Dimension Reduction
- 8. Unsupervised Learning-Auto Encoder
- 9. Introduction - Unsupervised Learning
- 10. Recursive Cascaded Networks for Unsupervised Medical Image Registration
- 更多相關文章...
- • WebSecurity - ConfirmAccount() - ASP.NET 教程
- • XQuery 添加元素 和屬性 - XQuery 教程
- • Java Agent入門實戰（一）-Instrumentation介紹與使用
- • Java Agent入門實戰（三）-JVM Attach原理與使用
相關標籤/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。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 【論文閱讀筆記】An Unsupervised Learning Model for Deformable Medical Image Registration
- 2. Multi-Modal Image Registration with Unsupervised Deep Learning
- 3. An Unsupervised Autoregressive Model for Speech Representation Learning
- 4. Supervised learning and Unsupervised learning
- 5. Clustering - Unsupervised learning introduction
- 6. Unsupervised Learning: Neighbor Embedding
- 7. Unsupervised Learning-Dimension Reduction
- 8. Unsupervised Learning-Auto Encoder
- 9. Introduction - Unsupervised Learning
- 10. Recursive Cascaded Networks for Unsupervised Medical Image Registration