Name
Abstract | ||
|---|---|---|
•An uncertainty estimation and segmentation module (UESM) is proposed, which can provide and speed up the uncertainty estimation for the UDA task.•An uncertainty-aware cross entropy loss is proposed to utilize the uncertainty maps to boost the segmentation performance on highly uncertain regions.•An uncertainty-aware self-training strategy is proposed to select the optimal target samples determined by uncertainty values.•An uncertainty feature recalibration module is proposed together with our adversarial learning block to minimize the cross-domain discrepancy.•The proposed method achieves the best performance on both cross-device and cross-modality datasets compared with the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.media.2020.101732 | Medical Image Analysis |
Keywords | DocType | Volume |
Uncertainty,Domain adaptation,Unsupervised segmentation,Deep learning | Journal | 64 |
ISSN | Citations | PageRank |
1361-8415 | 2 | 0.36 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Bian | 1 | 4 | 2.44 |
| Chenglang Yuan | 2 | 6 | 1.53 |
| Jiexiang Wang | 3 | 9 | 1.48 |
| Meng Li | 4 | 2 | 0.36 |
| Xin Yang | 5 | 175 | 12.96 |
| Shuang Yu | 6 | 7 | 3.15 |
| Kai Ma | 7 | 49 | 18.48 |
| Jin Yuan | 8 | 2 | 0.36 |
| Yefeng Zheng | 9 | 1391 | 114.67 |