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Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training

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arxiv 2109.14219 v1 pith:MDWXZL6A submitted 2021-09-29 cs.CV

Hexin Dong , Fei Yu , Jie Zhao , Bin Dong , Li Zhang This is my paper
classification cs.CV
keywords alignmentdomainpixelsegmentationself-trainingadaptationhrt2scans
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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.

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Cited by 1 Pith paper

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  1. IntraStyler: Intra-Domain Style Synthesis for Cross-Modality MRI Domain Adaptation

    cs.CV 2026-01 conditional novelty 7.0 of 10

    An exemplar-based style synthesis method that learns scanner-like style vectors via contrastive learning and uses them to generate diverse synthetic T2 images, improving downstream cross-modality segmentation.

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