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Unravelling the effect of data augmentation transformations in polyp segmentation
PURPOSE: Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying t...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671995/ https://www.ncbi.nlm.nih.gov/pubmed/32989680 http://dx.doi.org/10.1007/s11548-020-02262-4 |