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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| Aineistotyyppi: | Conference item |
| Kieli: | English |
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Springer Nature
2020
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