Background: Medical image segmentation plays a vital role in computer-aided diagnosis (CAD) systems. Both convolutional neural networks (CNNs) with strong local information extraction capacities and transformers with excellent global representation capacities have achieved remarkable performance in medical image segmentation. However, because of the semantic differences between local and global features, how to combine convolution and transformers effectively is an important challenge in medical image segmentation. Methods: In this paper, we proposed TransConver, a U-shaped segmentation network based on convolution and transformer for automatic and accurate brain tumor segmentation in MRI images. Unlike the recently proposed transformer and convolution based models, we proposed a parallel module named transformerconvolution inception (TC-inception), which extracts local and global information via convolution blocks and transformer blocks, respectively, and integrates them by a cross-attention fusion with global and local feature (CAFGL) mechanism. Meanwhile, the improved skip connection structure named skip connection with cross-attention fusion (SCCAF) mechanism can alleviate the semantic differences between encoder features and decoder features for better feature fusion. In addition, we designed 2D-TransConver and 3D-TransConver for 2D and 3D brain tumor segmentation tasks, respectively, and verified the performance and advantage of our model through brain tumor datasets. Results: We trained our model on 335 cases from the training dataset of MICCAI BraTS2019 and evaluated the model's performance based on 66 cases from MICCAI BraTS2018 and 125 cases from MICCAI BraTS2019. Our TransConver achieved the best average Dice score of 83.72% and 86.32% on BraTS2019 and BraTS2018, respectively. Conclusions: We proposed a transformer and convolution parallel network named TransConver for brain tumor segmentation. The TC-Inception module effectively extracts global information while retaining local details. The experimental results demonstrated that good segmentation requires the model to extract local fine-grained details and global semantic information simultaneously, and our TransConver effectively improves the accuracy of brain tumor segmentation.
Big data condition monitoring in the industrial of internet era is indispensable, and intelligent fault diagnosis plays an important role in it. The adversarial learning method is widely used because of its ability to extract domain invariant features to solve the variable speed fault diagnosis problem. However, its training process is often unstable and difficult to converge to the optimal solution, which brings great challenges to the fault detection of equipment. In view of this exasperating problem, a novel model, called deep domain adversarial method with central moment discrepancy, is proposed. The presented model mainly consists of four modules: a shared weight feature extraction network with wide convolution kernel, a supervised classification network, an adversarial domain classification network, and a CMD alignment network. Adversarial domain classification network is employed to extract features that have both category distinction and domain invariance in the process of mutual game learning between features of source domain and target domain. The CMD alignment network can be devoted to align the higher-order moments of two domain features to constrain the instability in adversarial learning. Through the above regularization method, the model shows a relatively stable and higher accuracy of transferring diagnosis in the non-standardized data. The public test data set and the private data set are applied to validate the model. The results show that the proposed model successfully solves the problem of training instability in adversarial learning and has a relatively high diagnostic accuracy.
Rolling bearings play a vital role in the overall operation of rotating machineries. In practical diagnosis, many learning methods for variable speed fault diagnosis ignore task-specific decision boundaries, which make it very difficult to match feature distributions between different domains completely. Therefore, an adversarial domain adaptation of asymmetric mapping with coral alignment (ADA-AMCA) is presented to dispose this problem. By using the asymmetric mapping feature extractor, more features of specific domain with obvious distinction can be extracted. Meanwhile, combining the maximum classifier discrepancy of deep transfer to form an adversarial approach, and the task-specific decision boundary is taken into account, the class-level alignment between the features of source domain and target domain is attempted. For the sake of preventing degenerate learning which is possibly caused by asymmetric mapping and adversarial learning, the model is constrained by deep coral to extract more domain invariant features. Experimental results show that the proposed method can solve the variable speed fault diagnosis problem well, with high transfer accuracy and strong generalization.
At present, most of the fault diagnosis methods with extensive research and good diagnostic effect are based on the premise that the sample distribution is consistent. However, in reality, the sample distribution of rotating machinery is inconsistent due to variable working conditions, and most of the fault diagnosis algorithms have poor diagnostic effects or even invalid. To dispose the above problems, a novel symmetric stacked autoencoder (NSSAE) for adversarial domain adaptation is proposed. Firstly, the symmetric stacked autoencoder network with shared weights is used as the feature extractor to extract features which can better express the original signal. Secondly, adding domain discriminator that constituting adversarial with feature extractor to enhance the ability of feature extractor to extract domain invariant features, thus confusing the domain discriminator and making it unable to correctly distinguish the features of the two domains. Finally, to assist the adversarial training, the maximum mean discrepancy (MMD) is added to the last layer of the feature extractor to align the features of the two domains in the high-dimensional space. The experimental results show that, under the condition of variable speed, the NSSAE model can extract domain invariant features to achieve the transfer between domains, and the transfer diagnosis accuracy is high and the stability is strong.
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