《MICCAI2019》Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmen
预备知识： CT缺乏对医学影像的软组织对比度的信息，这也使得在肿瘤边界的划分以及可视化方面变得困难 CT中部分难以被解释的特征，在CT学习提取MR图像信息的时候可以用来提供推断信息 CycleGAN思想： GANCycleGAN在我理解就是一个X->Y的单向GAN+Y->X的单向GAN，包括两个生成器G，F以及两个判别器Dx，Dy，在loss function上，一个GAN包括两个loss fun
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相关文章
- 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 分割论文
- 6. Integrating Thymeleaf with Spring
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- 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