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...
| Hlavní autoři: | , , , |
|---|---|
| Médium: | Conference item |
| Jazyk: | English |
| Vydáno: |
Springer Nature
2020
|