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...
| Egile Nagusiak: | , , , |
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| Formatua: | Conference item |
| Hizkuntza: | English |
| Argitaratua: |
Springer Nature
2020
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