Complementary consistency semi-supervised learning for 3D left atrial
image segmentation
- URL: http://arxiv.org/abs/2210.01438v5
- Date: Tue, 4 Apr 2023 13:09:22 GMT
- Title: Complementary consistency semi-supervised learning for 3D left atrial
image segmentation
- Authors: Hejun Huang, Zuguo Chen, Chaoyang Chen, Ming Lu and Ying Zou
- Abstract summary: CC-Net is a network for semi-supervised left atrium image segmentation.
It efficiently utilizes unlabeled data from the perspective of complementary information.
CC-Net has been validated on two public datasets.
- Score: 9.836802392009618
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A network based on complementary consistency training, called CC-Net, has
been proposed for semi-supervised left atrium image segmentation. CC-Net
efficiently utilizes unlabeled data from the perspective of complementary
information to address the problem of limited ability of existing
semi-supervised segmentation algorithms to extract information from unlabeled
data. The complementary symmetric structure of CC-Net includes a main model and
two auxiliary models. The complementary model inter-perturbations between the
main and auxiliary models force consistency to form complementary consistency.
The complementary information obtained by the two auxiliary models helps the
main model to effectively focus on ambiguous areas, while enforcing consistency
between the models is advantageous in obtaining decision boundaries with low
uncertainty. CC-Net has been validated on two public datasets. In the case of
specific proportions of labeled data, compared with current advanced
algorithms, CC-Net has the best semi-supervised segmentation performance. Our
code is publicly available at https://github.com/Cuthbert-Huang/CC-Net.
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