《MICCAI2019》Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmen
預備知識： CT缺乏對醫學影像的軟組織對比度的信息，這也使得在腫瘤邊界的劃分以及可視化方面變得困難 CT中部分難以被解釋的特徵，在CT學習提取MR圖像信息的時候可以用來提供推斷信息 CycleGAN思想： GANCycleGAN在我理解就是一個X->Y的單向GAN+Y->X的單向GAN，包括兩個生成器G，F以及兩個判別器Dx，Dy，在loss function上，一個GAN包括兩個loss fun
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相關文章
- 1. [MICCAI2019]Integrating Cross-modality Hallucinated MRI with CT to Aid Mediastinal Lung Tumor
- 2. LEARNING VOLUMETRIC SEGMENTATION FOR LUNG TUMOR
- 3. [MICCAI2019]Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT Using Two-Stream Chained
- 4. Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet
- 5. lung 分割論文
- 6. Integrating Thymeleaf with Spring
- 7. Automatic Tumor Segmentation from MRI scans 閱讀筆記
- 8. 3D MRI brain tumor segmentation using autoencoder regularization
- 9. Brain tumor segmentation with Deep Neural Networks
- 10. [MICCAI2019] Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-M