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。
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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
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