DDA-Net:Unsupervised Cross-modality Medical Image Segmentation via Dual Domain Adaptation

2021 ◽  
pp. 106531
Author(s):  
Xuesheng Bian ◽  
Xiongbiao Luo ◽  
Cheng Wang ◽  
Weiquan Liu ◽  
Xiuhong Lin
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
Medical Image Segmentation ◽  
Dual Domain

A multi-task unsupervised domain adaptation network for medical image segmentation

2021 ◽  
Author(s):  
Yuejing Shi ◽  
Fan Zhu ◽  
Yan Peng ◽  
Zhen Ye ◽  
Chaozheng Zhou
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
Medical Image Segmentation ◽  
Unsupervised Domain Adaptation

Semantic Consistent Unsupervised Domain Adaptation for Cross-Modality Medical Image Segmentation

2021 ◽  
pp. 201-210
Author(s):  
Guodong Zeng ◽  
Till D. Lerch ◽  
Florian Schmaranzer ◽  
Guoyan Zheng ◽  
Jürgen Burger ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
Medical Image Segmentation ◽  
Unsupervised Domain Adaptation

scholarly journals Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

2019 ◽  
Vol 33 ◽  
pp. 865-872 ◽  
Author(s):  
Cheng Chen ◽  
Qi Dou ◽  
Hao Chen ◽  
Jing Qin ◽  
Pheng-Ann Heng
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
Target Domain ◽  
Learning Procedure ◽  
The Neural Networks ◽  
Feature Adaptation ◽  
Segmentation Task

This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of crossmodality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.

scholarly journals A Novel Domain Adaptation Framework for Medical Image Segmentation

2019 ◽  
pp. 289-298 ◽  
Author(s):  
Amir Gholami ◽  
Shashank Subramanian ◽  
Varun Shenoy ◽  
Naveen Himthani ◽  
Xiangyu Yue ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
Medical Image Segmentation

scholarly journals Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

2020 ◽  
Vol 65 ◽  
pp. 101766 ◽  
Author(s):  
Yingda Xia ◽  
Dong Yang ◽  
Zhiding Yu ◽  
Fengze Liu ◽  
Jinzheng Cai ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
Medical Image Segmentation

scholarly journals Unsupervised Domain Adaptation with Dual-Scheme Fusion Network for Medical Image Segmentation

2020 ◽  
Author(s):  
Danbing Zou ◽  
Qikui Zhu ◽  
Pingkun Yan
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Network Performance ◽  
Domain Adaptation ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Tumor Segmentation ◽  
Target Domain ◽  
Brain Tumor Segmentation ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to alleviate the problem of retraining a pre-trained model when applying it to a different domain, which requires large amount of additional training data of the target domain. Such an objective is usually achieved by establishing connections between the source domain labels and target domain data. However, this imbalanced source-to-target one way pass may not eliminate the domain gap, which limits the performance of the pre-trained model. In this paper, we propose an innovative Dual-Scheme Fusion Network (DSFN) for unsupervised domain adaptation. By building both source-to-target and target-to-source connections, this balanced joint information flow helps reduce the domain gap to further improve the network performance. The mechanism is further applied to the inference stage, where both the original input target image and the generated source images are segmented with the proposed joint network. The results are fused to obtain more robust segmentation. Extensive experiments of unsupervised cross-modality medical image segmentation are conducted on two tasks -- brain tumor segmentation and cardiac structures segmentation. The experimental results show that our method achieved significant performance improvement over other state-of-the-art domain adaptation methods.

scholarly journals S-CUDA: Self-Cleansing Unsupervised Domain Adaptation for Medical Image Segmentation

2021 ◽  
pp. 102214
Author(s):  
Luyan Liu ◽  
Zhengdong Zhang ◽  
Shuai Li ◽  
Kai Ma ◽  
Yefeng Zheng
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
Medical Image Segmentation ◽  
Unsupervised Domain Adaptation
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