Unsupervised Domain Adaptation Based Image Synthesis and Feature Alignment for Joint Optic Disc and Cup Segmentation

2021 ◽  
pp. 1-1
Author(s):  
Haijun Lei ◽  
Weixin Liu ◽  
Hai Xie ◽  
Benjian Zhao ◽  
Guanghui Yue ◽  
...  
Keyword(s):  
Optic Disc ◽  
Domain Adaptation ◽  
Image Synthesis ◽  
Unsupervised Domain Adaptation ◽  
Feature Alignment

scholarly journals Progressive Feature Alignment for Unsupervised Domain Adaptation

2019 ◽  
Author(s):  
Chaoqi Chen ◽  
Weiping Xie ◽  
Wenbing Huang ◽  
Yu Rong ◽  
Xinghao Ding ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Feature Alignment

Informative Class-Conditioned Feature Alignment for Unsupervised Domain Adaptation

2021 ◽  
Author(s):  
Wanxia Deng ◽  
Yawen Cui ◽  
Zhen Liu ◽  
Gangyao Kuang ◽  
Dewen Hu ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Feature Alignment

Enhanced Feature Alignment for Unsupervised Domain Adaptation of Semantic Segmentation

2021 ◽  
pp. 1-1
Author(s):  
Tao Chen ◽  
Shuihua Wang ◽  
Qiong Wang ◽  
Zheng Zhang ◽  
Guosen Xie ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Unsupervised Domain Adaptation ◽  
Feature Alignment

RA-SIFA: Unsupervised domain adaptation multi-modality cardiac segmentation network combining parallel attention module and residual attention unit

2021 ◽  
pp. 1-14
Author(s):  
Tiejun Yang ◽  
Xiaojuan Cui ◽  
Xinhao Bai ◽  
Lei Li ◽  
Yuehong Gong
Keyword(s):  
Image Segmentation ◽  
Domain Adaptation ◽  
Mr Images ◽  
Unsupervised Domain Adaptation ◽  
Cardiac Image ◽  
Cardiac Segmentation ◽  
Study Results ◽  
Whole Heart ◽  
Cardiac Image Segmentation ◽  
Feature Alignment

BACKGROUND: Convolutional neural network has achieved a profound effect on cardiac image segmentation. The diversity of medical imaging equipment brings the challenge of domain shift for cardiac image segmentation. OBJECTIVE: In order to solve the domain shift existed in multi-modality cardiac image segmentation, this study aims to investigate and test an unsupervised domain adaptation network RA-SIFA, which combines a parallel attention module (PAM) and residual attention unit (RAU). METHODS: First, the PAM is introduced in the generator of RA-SIFA to fuse global information, which can reduce the domain shift from the respect of image alignment. Second, the shared encoder adopts the RAU, which has residual block based on the spatial attention module to alleviate the problem that the convolution layer is insensitive to spatial position. Therefore, RAU enables to further reduce the domain shift from the respect of feature alignment. RA-SIFA model can realize the unsupervised domain adaption (UDA) through combining the image and feature alignment, and then solve the domain shift of cardiac image segmentation in a complementary manner. RESULTS: The model is evaluated using MM-WHS2017 datasets. Compared with SIFA, the Dice of our new RA-SIFA network is improved by 8.4%and 3.2%in CT and MR images, respectively, while, the average symmetric surface distance (ASD) is reduced by 3.4 and 0.8mm in CT and MR images, respectively. CONCLUSION: The study results demonstrate that our new RA-SIFA network can effectively improve the accuracy of whole-heart segmentation from CT and MR images.

scholarly journals Exploiting Local Feature Patterns for Unsupervised Domain Adaptation

2019 ◽  
Vol 33 ◽  
pp. 5401-5408 ◽  
Author(s):  
Jun Wen ◽  
Risheng Liu ◽  
Nenggan Zheng ◽  
Qian Zheng ◽  
Zhefeng Gong ◽  
...  
Keyword(s):  
Negative Transfer ◽  
Domain Adaptation ◽  
Local Feature ◽  
Fine Grained ◽  
Unsupervised Domain Adaptation ◽  
Distribution Matching ◽  
Benchmark Datasets ◽  
Invariant Representations ◽  
Multi Mode ◽  
Feature Alignment

Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation methods focus on holistic feature alignment by matching source and target holistic feature distributions, without considering local features and their multi-mode statistics. We show that the learned local feature patterns are more generic and transferable and a further local feature distribution matching enables fine-grained feature alignment. In this paper, we present a method for learning domain-invariant local feature patterns and jointly aligning holistic and local feature statistics. Comparisons to the state-of-the-art unsupervised domain adaptation methods on two popular benchmark datasets demonstrate the superiority of our approach and its effectiveness on alleviating negative transfer.

scholarly journals Learning Domain Invariant Representation via Self-Rugularization

2021 ◽  
Vol 24 (4) ◽  
pp. 382-391
Author(s):  
Jaeguk Hyun ◽  
ChanYong Lee ◽  
Hoseong Kim ◽  
Hyunjung Yoo ◽  
Eunjin Koh
Keyword(s):  
Domain Adaptation ◽  
Regularization Methods ◽  
Invariant Representation ◽  
Large Dataset ◽  
Style Transfer ◽  
Unsupervised Domain Adaptation ◽  
Loss Term ◽  
Target Data ◽  
Small Dataset ◽  
Feature Alignment

Unsupervised domain adaptation often gives impressive solutions to handle domain shift of data. Most of current approaches assume that unlabeled target data to train is abundant. This assumption is not always true in practices. To tackle this issue, we propose a general solution to solve the domain gap minimization problem without any target data. Our method consists of two regularization steps. The first step is a pixel regularization by arbitrary style transfer. Recently, some methods bring style transfer algorithms to domain adaptation and domain generalization process. They use style transfer algorithms to remove texture bias in source domain data. We also use style transfer algorithms for removing texture bias, but our method depends on neither domain adaptation nor domain generalization paradigm. The second regularization step is a feature regularization by feature alignment. Adding a feature alignment loss term to the model loss, the model learns domain invariant representation more efficiently. We evaluate our regularization methods from several experiments both on small dataset and large dataset. From the experiments, we show that our model can learn domain invariant representation as much as unsupervised domain adaptation methods.

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