論文閱讀——Deformable Medical Image Registration Using Generative Adversarial Networks
基於GAN實現可變醫學圖像配準
from IBM Research
ISBI2018 Abstract 常規的圖像配準都採用迭代的方式，十分耗時。而近期深度學習技術可以在迭代中提取的深層特徵進行圖像配準。本文藉助生成對抗網絡從而避免了配準過程中耗時良久的迭代過程，直接生成配準後的圖像。通過在GAN的損失函數中增加適當約束可以在一秒內生成精確的圖像，在多模態的視網膜圖像及心室MR圖像中驗證了本文
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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. 論文閱讀——Synthetic Medical Images from Dual Generative Adversarial Networks
- 3. Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
- 4. 論文閱讀之《Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network》
- 5. 論文閱讀《Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks》
- 6. 論文閱讀——Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks
- 7. 論文閱讀——Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- 8. 論文閱讀——《Generative Adversarial Nets》
- 9. 論文閱讀：deformable convolutional networks
- 10. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks