Annotation-efficient deep learning for automatic medical image segmentation
Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets.
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-10-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-26216-9 |