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Seamless lesion insertion for data augmentation in CAD training
The performance of a classifier is largely dependent on the size and representativeness of data used for its training. In circumstances where accumulation and/or labeling of training samples is difficult or expensive, such as medical applications, data augmentation can potentially be used to allevia...
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| Publicado no: | IEEE Trans Med Imaging |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5509514/ https://ncbi.nlm.nih.gov/pubmed/28113310 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2016.2640180 |
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