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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Détails bibliographiques
| Publié dans: | Neurocomputing. - 1998. - 335(2019) vom: 03. Sept., Seite 34-45
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| Auteur principal: |
Wang, Guotai
(Auteur) |
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| Autres auteurs: |
Li, Wenqi,
Aertsen, Michael,
Deprest, Jan,
Ourselin, Sébastien,
Vercauteren, Tom |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2019
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| Accès à la collection: | Neurocomputing
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| Sujets: | Journal Article
Convolutional neural networks
Data augmentation
Medical image segmentation
Uncertainty estimation |
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