Name
Title | ||
|---|---|---|
Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning. |
Abstract | ||
|---|---|---|
Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to n target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-33391-1_7 | DART/MIL3ID@MICCAI |
Keywords | Field | DocType |
Adversarial learning,Brain MR,Domain adaptation | Brain mri,Pattern recognition,Domain adaptation,Segmentation,Computer science,Multi domain,Artificial intelligence,Supervised training,Labeled data,Paired Data,Hyperintensity | Conference |
Volume | Citations | PageRank |
2019 | 2 | 0.42 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mauricio Orbes-Arteaga | 1 | 2 | 0.42 |
| Thomas Varsavsky | 2 | 3 | 2.13 |
| Sudre Carole H. | 3 | 132 | 12.86 |
| Zach Eaton-Rosen | 4 | 65 | 7.69 |
| Lewis J. Haddow | 5 | 2 | 0.42 |
| Lauge Sørensen | 6 | 215 | 17.78 |
| Mads Nielsen | 7 | 1197 | 156.23 |
| Akshay Pai | 8 | 24 | 8.06 |
| Sebastien Ourselin | 9 | 66 | 6.31 |
| Marc Modat | 10 | 898 | 72.33 |
| Parashkev Nachev | 11 | 3 | 2.81 |
| Cardoso M. Jorge | 12 | 64 | 13.70 |