Background Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively. Methods A method for detecting pulmonary nodules based on an improved neural network is presented in this paper. Nodules are clusters of tissue with a diameter of 3 mm to 30 mm in the pulmonary parenchyma. Because pulmonary nodules are similar to other lung structures and have a low density, false positive nodules often occur. Thus, our team proposed an improved convolutional neural network (CNN) framework to detect nodules. First, a nonsharpening mask is used to enhance the nodules in computed tomography (CT) images; then, CT images of 512×512 pixels are segmented into smaller images of 96×96 pixels. Second, in the 96×96 pixel images which contain or exclude pulmonary nodules, the plaques corresponding to positive and negative samples are segmented. Third, CT images segmented into 96×96 pixels are down-sampled to 64×64 and 32×32 size respectively. Fourth, an improved fusion neural network structure is constructed that consists of three three-dimensional convolutional neural networks, designated as CNN-1, CNN-2, and CNN-3, to detect false positive pulmonary nodules. The networks’ input sizes are 32×32×32, 64×64×64, and 96×96×96 and include 5, 7, and 9 layers, respectively. Finally, we use the AdaBoost classifier to fuse the results of CNN-1, CNN-2, and CNN-3. We call this new neural network framework the Amalgamated-Convolutional Neural Network (A-CNN) and use it to detect pulmonary nodules. Findings Our team trained A-CNN using the LUNA16 and Ali Tianchi datasets and evaluated its performance using the LUNA16 dataset. We discarded nodules less than 5mm in diameter. When the average number of false positives per scan was 0.125 and 0.25, the sensitivity of A-CNN reached as high as 81.7% and 85.1%, respectively.
As one of the most important research fields in the brain-computer interface (BCI) field, electroencephalogram (EEG) classification has a wide range of application values. However, for the EEG signal, it is difficult for the traditional neural networks to capture the characteristics of the EEG signal more comprehensively from the time and space dimensions, which has a certain effect on the accuracy of EEG classification. To solve this problem, we can improve the accuracy of classification via end-to-end learning of the time and space dimensions of EEG. In this paper, a new type of EEG classification network, the separable EEGNet (S-EEGNet), is proposed based on Hilbert-Huang transform (HHT) and a separable convolutional neural network (CNN) with bilinear interpolation. The EEG signal is transformed into time-frequency representation by HHT, which allows the EEG signal to be better described in the frequency domain. Then, the depthwise and pointwise elements of the network are combined to extract the feature map. The displacement variable is added by the bilinear interpolation method to the convolution layer of the separable CNN, allowing the free deformation of the sampling grid. The deformation depends on the local, dense, and adaptive input characteristics of the EEG data. The network can learn from the time and space dimensions of EEG signals end to end to extract features to improve the accuracy of EEG classification. To show the effectiveness of S-EEGNet, the team used this method to test two different types of EEG public datasets (motor imagery classification and emotion classification). The accuracy of motor imagery classification is 77.9%, and the accuracy of emotion classification is 89.91%, and 88.31%, respectively. The experimental results showed that the classification accuracy of S-EEGNet improved by 3.6%, 1.15%, and 1.33%, respectively.
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guarantee of the global model, however, it is still unclear how each individual client influences the collaborative training process. In this work, we defined a new notion, called {\em Fed-Influence}, to quantify this influence over the model parameters, and proposed an effective and efficient algorithm to estimate this metric. In particular, our design satisfies several desirable properties: (1) it requires neither retraining nor retracing, adding only linear computational overhead to clients and the server; (2) it strictly maintains the tenets of federated learning, without revealing any client's local private data; and (3) it works well on both convex and non-convex loss functions, and does not require the final model to be optimal. Empirical results on a synthetic dataset and the FEMNIST dataset demonstrate that our estimation method can approximate Fed-Influence with small bias. Further, we show an application of Fed-Influence in model debugging.
Self-supervised contrastive learning has recently been shown to be very effective in preventing deep networks from overfitting noisy labels. Despite its empirical success, the theoretical understanding of the effect of contrastive learning on boosting robustness is very limited. In this work, we rigorously prove that the representation matrix learned by contrastive learning boosts robustness, by having: (i) one prominent singular value corresponding to every sub-class in the data, and remaining significantly smaller singular values; and (ii) a large alignment between the prominent singular vector and the clean labels of each subclass. The above properties allow a linear layer trained on the representations to quickly learn the clean labels, and prevent it from overfitting the noise for a large number of training iterations. We further show that the low-rank structure of the Jacobian of deep networks pre-trained with contrastive learning allows them to achieve a superior performance initially, when fine-tuned on noisy labels. Finally, we demonstrate that the initial robustness provided by contrastive learning enables robust training methods to achieve stateof-the-art performance under extreme noise levels, e.g., an average of 27.18% and 15.58% increase in accuracy on CIFAR-10 and CIFAR-100 with 80% symmetric noisy labels, and 4.11% increase in accuracy on WebVision. Noise Type Sym Asym Sym Asym Noise Ratio 20 50 80 40 20 50 80 40 F-correction 85.1 ± 0.4 76.0 ± 0.2 34.8 ± 4.5 83.6 ± 2.2 55.8 ± 0.5 43.3 ± 0.7 − 42.3 ± 0.7 Decoupling 86.7 ± 0.3 79.3 ± 0.6 36.9 ± 4.6 75.3 ± 0.8 57.6 ± 0.5 45.7 ± 0.4 − 43.1 ± 0.4 Co-teaching 89.1 ± 0.3 82.1 ± 0.6 16.2 ± 3.2 84.6 ± 2.8 64.0 ± 0.3 52.3 ± 0.4 − 47.7 ± 1.2 MentorNet 88.4 ± 0.5 77.1 ± 0.4 28.9 ± 2.3 77.3 ± 0.8 63.0 ± 0.4 46.4 ± 0.4 − 42.4 ± 0.5 D2L 86.1 ± 0.4 67.4 ± 3.6 10.0 ± 0.1 85.6 ± 1.2 12.5 ± 4.2 5.6 ± 5.4 − 14.1 ± 5.8 INCV 89.7 ± 0.2 84.8 ± 0.3 52.3 ± 3.5 86.0 ± 0.5 60.2 ± 0.2 53.1 ± 0.4 − 50.7 ± 0.2 T-Revision 79.3 ± 0.5 78.5 ± 0.6 36.2 ± 1.6 76.3 ± 0.8 52.4 ± 0.3 37.6 ± 0.3 − 32.3 ± 0.4 L DMI 84.3 ± 0.4 78.8 ± 0.5 20.9 ± 2.2 84.8 ± 0.7 56.8 ± 0.4 42.2 ± 0.5 − 39.5 ± 0.4 Crust * 85.3 ± 0.5 86.8 ± 0.3 33.8 ± 1.3 76.7 ± 3.4 62.9 ± 0.3 55.5 ± 1.1 18.5 ± 0.8 52.5 ± 0.4 Mixup 89.7 ± 0.7 84.5 ± 0.3 40.7 ± 1.1 86.3 ± 0.1 64.0 ± 0.4 53.4 ± 0.5 15.1 ± 0.1 54.4 ± 2.0 ELR * 90.6 ± 0.6 87.7 ± 1.0 69.5 ± 5.0 86.6 ± 2.9 63.6 ± 1.7 52.5 ± 4.2 23.4 ± 1.9 59.7 ± 0.1 CL+E2E * 88.8 ± 0.5 82.8 ± 0.2 72.0 ± 0.3 83.5 ± 0.5 63.5 ± 0.2 56.1 ± 0.3 36.7 ± 0.3 52.4 ± 0.2 CL+Crust * 86.5 ± 0.7 87.6 ± 0.3 77.9 ± 0.3 85.9 ± 0.4 63.0 ± 0.8 58.3 ± 0.1 34.8 ± 1.5 53.3 ± 0.7 CL+Mixup * 90.8 ± 0.2 84.6 ± 0.4 74.8 ± 0.3 87.5 ± 1.3 64.4 ± 0.4 55.5 ± 0.1 30.3 ± 0.4 55.5 ± 0.8 CL+ELR * 91.3 ± 0.0 89.1 ± 0.1 77.7 ± 0.2 89.7 ± 0.3 64.7 ± 0.2 55.6 ± 0.2 35.9 ± 0.3 63.6 ± 0.1
In automatic control systems, negative feedback control has the advantage of maintaining a steady state, while positive feedback control can enhance some activities of the control system. How to design a controller with both control modes is an interesting and challenging problem. Motivated by it, on the basis idea of catastrophe theories, taking positive feedback and negative feedback as two different states of the system, an adaptive alternating positive and negative feedback (APNF) control model with the advantages of two states is proposed. By adaptively adjusting the relevant parameters of the constructed symmetric catastrophe function and the learning rule based on error and forward weight, the two states can be switched in the form of catastrophe. Through the Lyapunov stability theory, the convergence of the proposed adaptive APNF control model is proven, which indicates that system convergence can be guaranteed by selecting appropriate parameters. Moreover, we present theoretical proof that the negative feedback system with negative parameters can be equivalent to the positive feedback system with positive parameters. Finally, the results of the simulation example show that APNF control has satisfactory performance in response speed and overshoot.
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