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Anatomy-Regularized Representation Learning for Cross-Modality Medical Image Segmentation
An increasing number of studies are leveraging unsupervised cross-modality synthesis to mitigate the limited label problem in training medical image segmentation models. They typically transfer ground truth annotations from a label-rich imaging modality to a label-lacking imaging modality, under an...
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| 出版年: | IEEE Trans Med Imaging |
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| 主要な著者: | , , , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8120796/ https://ncbi.nlm.nih.gov/pubmed/32956048 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2020.3025133 |
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