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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Detalles Bibliográficos
Autores principales: Wang, Guotai, Li, Wenqi, Aertsen, Michael, Deprest, Jan, Ourselin, Sébastien, Vercauteren, Tom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783308/
https://www.ncbi.nlm.nih.gov/pubmed/31595105
http://dx.doi.org/10.1016/j.neucom.2019.01.103

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6783308/
https://www.ncbi.nlm.nih.gov/pubmed/31595105
http://dx.doi.org/10.1016/j.neucom.2019.01.103

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