ELASTIC REGISTRATION OF MEDICAL IMAGES WITH GANS
一、Motivation 我們的方法使用生成式對抗網絡(GANs)，消除了耗時的迭代方法。 直接預測變形場用於圖像配準 二、Method 作者首先提到現有幾種Gan的缺點。 Conditional Gans(cGANs) 在cGANs中，輸出取決於輸入圖像和隨機噪聲向量，需要訓練圖像對。 cyclic GANs (cycGANs) cycGANs不需要訓練圖像, 但加強變形場的一致性。 把這兩
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相關文章
- 1. 【論文閱讀】10-Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Out
- 2. [Style Transfer]——GANs for Medical Image Analysis
- 3. 分割網絡綜述——Deep Semantic Segmentation of Natural and Medical Images: A Review
- 4. 醫學圖像分割--Segmentation of Medical Ultrasound Images Using Convolutional Neural Networks
- 5. [Style Transfer]——MedGAN: Medical Image Translation using GANs
- 6. Recursive Cascaded Networks for Unsupervised Medical Image Registration
- 7. 《Data-Driven Enhancement of Blurry Retinal Images via GANs》--閱讀筆記-MICCAI2019
- 8. SLD_UC registration failes with return code 748
- 9. Image Registration
- 10. Working with Docker Images