Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the stru...Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.展开更多
为了有效利用少量的医学图像标签数据和大量的无标签数据,提出了一种基于半监督学习和生成对抗网络的医学图像融合算法。所提生成对抗网络融合架构包含1个生成器网络和2个判别器网络。采用半监督学习策略对所提网络进行训练,主要包括监...为了有效利用少量的医学图像标签数据和大量的无标签数据,提出了一种基于半监督学习和生成对抗网络的医学图像融合算法。所提生成对抗网络融合架构包含1个生成器网络和2个判别器网络。采用半监督学习策略对所提网络进行训练,主要包括监督训练、无监督训练、参数微调等3个阶段。此外,生成器由面向融合任务的U-Net和squeeze and excitation通道注意力模块组成,而判别器含有3层卷积层、1层全连接层及sigmoid激活输出层。在各种不同模态医学图像上的实验结果表明,与现有的6种基于深度学习的算法相比,所提算法的主观视觉效果和客观性能指标都有一定竞争力。相关消融实验也验证了半监督学习策略能强化生成网络的性能,提高融合图像的质量。展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.91120305,61272251the National Basic Research 973 Program of China under Grant No.2015CB856004
文摘Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.
文摘为了有效利用少量的医学图像标签数据和大量的无标签数据,提出了一种基于半监督学习和生成对抗网络的医学图像融合算法。所提生成对抗网络融合架构包含1个生成器网络和2个判别器网络。采用半监督学习策略对所提网络进行训练,主要包括监督训练、无监督训练、参数微调等3个阶段。此外,生成器由面向融合任务的U-Net和squeeze and excitation通道注意力模块组成,而判别器含有3层卷积层、1层全连接层及sigmoid激活输出层。在各种不同模态医学图像上的实验结果表明,与现有的6种基于深度学习的算法相比,所提算法的主观视觉效果和客观性能指标都有一定竞争力。相关消融实验也验证了半监督学习策略能强化生成网络的性能,提高融合图像的质量。
