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Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training by Dong, Hexin; Yu, Fei; Zhao, Jie; Dong, Bin; Zhang, Li is a scholarly article available to read on EtoBox.

What is Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training about?

This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training. Pixel alignment transfers ceT1 scans to hrT2 modality, helping to reduce domain shift in the training segmentation model. Self-training adapts the decision boundary of the segmentation network to fit the distribution of hrT2 scans. Experiment results show that PAST has outperformed the non-UDA baseline significantly, and it received rank-2 on CrossMoDA validation phase Leaderboard with a mean Dice score of 0.8395.

Author

Dong, Hexin; Yu, Fei; Zhao, Jie; Dong, Bin; Zhang, Li

Published

2021

Language

EN