Medical Image Synthesis with Context-Aware Generative Adversarial Networks （簡介）
本文是關於MRT-to-CT。 首先，提出一個基本的三維FCN結構來估計從MRI得到的CT圖像。三維操作可以更好地對三維空間信息進行建模，從而可以解決切片間的不連續問題。其次，利用對抗式訓練策略對所設計的網絡進行訓練。還在生成器的損失函數中加入了一個圖像梯度差項（image gradient difference），目的是保持產生的CT的銳度。最後，使用Auto-Conte
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相關文章
- 1. Generative Adversarial Text to Image Synthesis
- 2. Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
- 3. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 4. StackGAN（2017）: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
- 5. StackGAN++《StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks》探析
- 6. text to image（二）:《Generative Adversarial Text to Image Synthesis》
- 7. Generative Adversarial Text to Image Synthesis 論文解讀
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- 9. Generative Adversarial Networks for Hyperspectral Image Classification
- 10. Binary Generative Adversarial Networks for Image Retrieval