论文标题
部分可观测时空混沌系统的无模型预测
Complementary consistency semi-supervised learning for 3D left atrial image segmentation
论文作者
论文摘要
已经提出了一个基于互补一致性训练的网络，称为CC-NET，用于半监督左心房图像分割。 CC-NET从互补信息的角度有效地利用了未标记的数据来解决现有半监督分段算法能力有限的问题，以从未标记的数据中提取信息。 CC-NET的互补对称结构包括一个主模型和两个辅助模型。主模型和辅助模型之间的互补模型互操作扰动，形成互补的一致性。两个辅助模型获得的互补信息有助于主模型有效地关注模棱两可的区域，而模型之间的一致性对于获得不确定性低的决策边界是有利的。 CC-NET已在两个公共数据集上进行了验证。在特定比例的标记数据的情况下，与当前的高级算法相比，CC-NET具有最佳的半监督分割性能。我们的代码可在https://github.com/cuthbert-huang/cc-net上公开获取。
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.
