Uncertainty estimates as data selection criteria to boost omni-supervised learning

Uncertainty estimates as data selection criteria to boost omni-supervised learning

For many medical applications, large quantities of imaging data are routinely obtained but it can be difficult and time-consuming to obtain high-quality labels for that data. We propose a novel uncertainty-based method to improve the performance of segmentation networks when limited manual labels ar...

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Dades bibliogràfiques
Autors principals:	Venturini, L, Papageorghiou, AT, Noble, JA, Namburete, AIL
Format:	Conference item
Idioma:	English
Publicat:	Springer Nature 2020

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