[MICCAI2019] Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-M
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation 作者：Junlin Yang，yale Intro CT便宜快速，但有輻射且對比度低；MRI對比度高，無輻射，但成本高，不易得到。在實際治療中，CT和MRI都需要，且需要對
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相關文章
- 1. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations
- 2. Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver
- 3. 【論文筆記】Unsupervised Domain-Specific Deblurring via Disentangled Representations
- 4. [論文筆記]Unsupervised Domain-Specific Deblurring via Disentangled Representations(CVPR2019)
- 5. [MICCAI2019]Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation
- 6. A DIRT-T APPROACH TO UNSUPERVISED DOMAIN ADAPTATION
- 7. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 8. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 9. Geodesic flow kernel for unsupervised domain adaptation
- 10. Unsupervised Domain Adaptation by Backpropagation(2015)