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Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model (epistemic) and image-based (aleatoric) uncertainties. In this work, we analyze these diff...
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| Publicado en: | Neurocomputing |
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| Autores principales: | , , , , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6783308/ https://ncbi.nlm.nih.gov/pubmed/31595105 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.neucom.2019.01.103 |
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