Data privacy protection domain adaptation by roughing and finishing stage
The automatic segmentation of organs or tissues is crucial for early diagnosis and treatment. Existing deep learning methods either need massive annotation data or use Unsupervised Domain Adaptation (UDA) approaches with labeled source domain data to train a model for unlabeled target domain data. T...
Main Authors: | Yuan, Liqiang, Erdt, Marius, Li, Ruilin, Siyal, Mohammed Yakoob |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/172253 |
Similar Items
-
Domain‐invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation
by: Xingmei Wang, et al.
Published: (2020-12-01) -
Scale variance minimization for unsupervised domain adaptation in image segmentation
by: Guan, Dayan, et al.
Published: (2022) -
Multi-level adversarial network for domain adaptive semantic segmentation
by: Huang, Jiaxing, et al.
Published: (2022) -
ADAST: Attentive cross-domain EEG-based sleep staging framework with iterative self-training
by: Eldele, Emadeldeen, et al.
Published: (2023) -
Adaptive Contrastive Learning with Label Consistency for Source Data Free Unsupervised Domain Adaptation
by: Xuejun Zhao, et al.
Published: (2022-06-01)