Dispensed Transformer Network for Unsupervised Domain Adaptation
- URL: http://arxiv.org/abs/2110.14944v1
- Date: Thu, 28 Oct 2021 08:27:44 GMT
- Title: Dispensed Transformer Network for Unsupervised Domain Adaptation
- Authors: Yunxiang Li, Jingxiong Li, Ruilong Dan, Shuai Wang, Kai Jin, Guodong
Zeng, Jun Wang, Xiangji Pan, Qianni Zhang, Huiyu Zhou, Qun Jin, Li Wang, Yaqi
Wang
- Abstract summary: A novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.
Our proposed network achieves the best performance in comparison with several state-of-the-art techniques.
- Score: 21.256375606219073
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Accurate segmentation is a crucial step in medical image analysis and
applying supervised machine learning to segment the organs or lesions has been
substantiated effective. However, it is costly to perform data annotation that
provides ground truth labels for training the supervised algorithms, and the
high variance of data that comes from different domains tends to severely
degrade system performance over cross-site or cross-modality datasets. To
mitigate this problem, a novel unsupervised domain adaptation (UDA) method
named dispensed Transformer network (DTNet) is introduced in this paper. Our
novel DTNet contains three modules. First, a dispensed residual transformer
block is designed, which realizes global attention by dispensed interleaving
operation and deals with the excessive computational cost and GPU memory usage
of the Transformer. Second, a multi-scale consistency regularization is
proposed to alleviate the loss of details in the low-resolution output for
better feature alignment. Finally, a feature ranking discriminator is
introduced to automatically assign different weights to domain-gap features to
lessen the feature distribution distance, reducing the performance shift of two
domains. The proposed method is evaluated on large fluorescein angiography (FA)
retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used
cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate
that our proposed network achieves the best performance in comparison with
several state-of-the-art techniques.
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