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Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop...
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| Publicado no: | Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018) |
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| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6545481/ https://ncbi.nlm.nih.gov/pubmed/31172133 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/978-3-030-00889-5_38 |
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