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...
| প্রধান লেখক: | , , , |
|---|---|
| বিন্যাস: | Conference item |
| ভাষা: | English |
| প্রকাশিত: |
Springer Nature
2020
|