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Natural Language processing (ICNLP), International Conference on

作者： Xiaoying Pan Yaya Mu Sun Jia Han Chen Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Colorectal cancer is a disease with a very high mortality rate, making timely diagnosis and prevention a crucial task. In clinical practice, the detection of intestinal polyps presents challenges due to their small size, complex background, and large span in target scale, making it difficult to accurately detect small targets. Existing detectors struggle to adaptively extract discriminative features, and their detection heads lack specificity, leading to difficulties in accurately identifying polyps against similar intestinal wall backgrounds. To address this issue, we propose a Bi-level Routing Attention and Asymmetric Dual-Head based framework for intestinal polyp detection(BRA-LADH). This approach enhances the detection capabilities of the branches through strategies such as global information fusion and adaptive sample selection. It dynamically sets thresholds for each target, ultimately solving the problems of missed and false detections caused by high similarity between the foreground and background and large variations in target scale, thus improving detection accuracy. Qualitative and quantitative experiments on three datasets demonstrate that this method outperforms existing ones, showing strong stability and generalization performance.

关键词： Training Head Prevention and mitigation Detectors Routing Feature extraction Stability analysis Magnetic heads Natural language processing Lesions

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A Model that Learns Discriminative Features for Automatic Sleep Stage Classification Based on Raw Single-Channel EEG

SSRN

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SSRN 2023年

作者： Heng, Xia Wang, Miao Wang, Zhong-Min Zhang, Jie He, Lang Fan, Lin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China

Sleep staging is the basis of sleep quality assessment. In the process of scoring each sleep stage, some automatic sleep staging models often fail to effectively capture the more accurate long-range correlation coupling between the input sleep EEG signals and the output sleep stage, which leads to the extracted features cannot effectively distinguish the different sleep stage. In order to alleviate the long-term time series problem of sleep EEG signals and learn the discriminative features of each sleep stage in sleep EEG signals, we propose an end-to-end automatic sleep stage classification method based on raw single-channel sleep EEG signals, which can improve the attention to the discriminative features of each sleep stage. In the construction of the model, in order to learn the source domain information of the key part of the sleep EEG signal, SE-Block is added to Convolutional Neural network (CNN) to highlight the time-frequency domain features related to the sleep stage. Meanwhile, the attention mechanism is added to the decoding part of Bidirectional Recurrent Unit (Bi-GRU) to enhance the long-term memory ability of Bi-GRU and learn the conversion rules between sleep stages. To validate the accuracy and stability of the model, the Fpz-Cz channel and Pz-Oz channel EEG signals in the Sleep-EDF sleep dataset are used for 10-fold cross-validation. The classification accuracies obtained are 88.48% and 87.56%, respectively. The results show that our model has stronger ability to extract discriminative features, better representation ability, more stable performance and relatively simple structure. © 2023, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Shadow Removal from a Single Image Using an Enhanced Generative Adversarial network

Shadow Removal from a Single Image Using an Enhanced Generat...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jing Zhang Weiwei Kong College of Computer Science Xi’an University of Posts and Telecommunications Xi’an China the Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

Image shadow removal is challenging due to the diversity of shadows and the dependence on the background of the area covered by shadows. In this paper, we design an enhanced generative adversarial network (EGAN) to handle the shadow removal task. EGAN is a complicated generative adversarial network that may exploit previous info, like shadow masks and lightness estimation, for improving recovery capability. In addition, EGAN can effectively utilize the details of shadow regions and eliminate artifacts. EGAN owns three main advantages. First, EGAN employs stricter rules for discriminators, which can upgrade the instability of traditional GAN. Second, EGAN involves a completely unique enhanced attention module, which may efficiently pick up different types of previous info to accelerate the learning efficiency of image reconstruction. Finally, EGAN is essentially an original cascaded network, that is, DRDU, which is beneficial for efficiently detecting hierarchical features of image recovery. Simulation experiments are conducted to verify the effectiveness of the proposed method on two benchmark datasets including SRD and ISTD, the strengths of our proposed method are indicated over the current representative ones according to subjective performance and objective estimation indexes.

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Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning

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Computers in Biology and Medicine 2024年 174卷 108428-108428页

作者： Xu, Xuebin Liu, Dehua Huang, Guohua Wang, Muyu Lei, Meng Jia, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China Weinan Central Hospital Shaanxi Xi'an714099 China

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR. © 2024 The Authors

关键词： Computer aided diagnosis

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LFD-RSNet: Lightweight Face Detection network for Rainy Weather Scenes

LFD-RSNet: Lightweight Face Detection Network for Rainy Weat...

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Natural Language processing (ICNLP), International Conference on

作者： Xiaoying Pan Yamie Ding Nijuan Zhang Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In response to the problem of low detection accuracy of lightweight models for face detection in complex rainy scenes, this paper proposes a face detection network FDL-RSNet for complex rainy scenes. This network integrates an adaptive image denoising module, an optimizer module, an HCA attention module, and a balanced branching strategy. The FDL-RSNet network can effectively address various challenges in complex rainy backgrounds, such as raindrop noise, differences in image lighting and contrast, and face detection challenges caused by low-quality images with dense targets. Experimental results on the UFDD Rain datasets show that FDL-RSNet has superior face detection capabilities in real-world unconstrained complex rainy scenes, Proved its effectiveness and reliability under such highly challenging conditions.

关键词： Adaptation models Rain Adaptive systems Noise Lighting Natural language processing Face detection Reliability Meteorology Image denoising

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Improved Siamese network for Video-Based Person Re-identification

Improved Siamese Network for Video-Based Person Re-identific...

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International Conference on networking and network Applications (NaNA)

作者： Zhongmin Wang Lanlan Cai Na Duan Lin Fan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

In the field of human-computer interaction and visual surveillance, person re-identification is undoubtedly a key task. In this paper, we propose improved Siamese network for video-based person re-identification, which combines the verification model and the classification model, in a way that all the supervision information of the samples is fully utilized. The key frame extraction is able to select informative frames over the sequence, which removes occluded frames according to optical flow vector and measures the similarity of two adjacent frames' contents in terms of the block mutual information entropy. A convolutional network and long short-term memory joint training is used as a feature extractor to re-identify using the improved Siamese network framework. Experimental data on the iLIDS-VID and PRID-2011 datasets show that this approach is superior to existing video-based re-identification methods.

关键词： Feature extraction Optical flow Mutual information data mining Entropy Logic gates Cameras

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A Multi-Objective Evolutionary Stacking Integration Approach for Survival Prediction in GBM Patients

A Multi-Objective Evolutionary Stacking Integration Approach...

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Natural Language processing (ICNLP), International Conference on

作者： Fangyuan Du Hao Chen Kunjun Chen Xuelian Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

To address the challenge of predicting survival rates for patients with Glioblastoma (GBM) after radiation therapy, we propose a two-stage integrated learning method that is based on Stacking and multi-objective differential evolution algorithm. Our aim is to achieve a balance between accuracy and interpretability. We have created a framework for survival prediction using radiomics features of gliomas that are imaged in subregions. Our method constructs a model for predicting GBM survival and visualizes decision rules that are easy to interpret. The method involves two primary steps: 1) Various combinations of MRI sequences and glioma subregions were evaluated to determine the optimal sequence, and the GBM long and short survival classification task in the first stage of the task was achieved by compressing features using spectral clustering. 2) An evolutionary integrated learning method based on Stacking was proposed. It uses a Random Forest classifier as the basic learning module of the integrated model and relies on the multi-objective optimization algorithm NSGA-II to continuously iterate with the aim of maximizing the regression accuracy while minimizing the model's integration complexity. We used 119 GBM patients in the BraTS2020 dataset as our study data. Based on the five-fold cross-validation, the survival classification Accuracy and Recall of our long- and short-term classification algorithm for GBM patients were 0.981 and 0.943, respectively; and the Mean-Square Error (MSE) of the survival prediction of GBM patients with the stack-based evolutionary integration method was 55713.08. The experimental results showed that our proposed method for GBM survival days prediction is significantly better than existing Machine Learning (ML) methods. This finding enables us to stratify patients more effectively and provides a more reliable basis for achieving personalized treatment for GBM patients.

关键词： Learning systems Accuracy Protocols Stacking Semantics Classification algorithms Radiation therapy Reliability Random forests Radiomics

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3D Hand Pose Estimation Based on External Attention

3D Hand Pose Estimation Based on External Attention

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Natural Language processing (ICNLP), International Conference on

作者： Shoukun Li Xiaoying Pan Beibei Wang Jialong Gao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

3D hand pose for a single depth image is an important topic in computer vision and human-computer interaction, and although significant progress has been made in this field in recent years, there is still room for improvement in accuracy for some specific application scenarios. To address this problem, a 3D hand pose estimation algorithm based on external attention is proposed. First, the target features are extracted by an hourglass network; then, a HEM (Hard Example Mining) loss based on a mean-variance loss function is proposed, which firstly calculates the L2 loss values of all N keypoints, and then sorts these loss values, and back-propagates the gradient only to the first m loss values. Meanwhile, external attention is introduced to enhance the ability to perceive the global information of the target, and the recognition ability of the features is improved by giving the features different influences through the attention weights. Experimental results show that the algorithm achieves an average distance error of 5.42 mm on the ICVL dataset and 7.11 mm on the MSRA dataset, which further improves the detection performance of the 3D hand pose estimation algorithm.

关键词： Human computer interaction Computer vision Three-dimensional displays Accuracy Target recognition Pose estimation Feature extraction Prediction algorithms Natural language processing

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Causal Logic Discovery and Rule Validation Using Evolutionary Integration Model Global Fit U-Net

Causal Logic Discovery and Rule Validation Using Evolutionar...

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Natural Language processing (ICNLP), International Conference on

作者： Xuelian Yang Fangyuan Du Kunjun Chen Hao Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to explore the correlation between different MRI sequences and the results of U-Net segmentation of glioma subregions, this paper proposes an interpretable method based on an evolutionary integration algorithm for logical discovery of the segmentation process of U-Net. Our approach consists of three steps: 1) Global fitting of the U-Net model to the segmentation results of gliomas using a dual evolutionary algorithm to generate a fitted model with both accuracy and interpretability.2) Extracting decision rules from the fitted model according to a specific target interpretable region and generating a complete set of interpretable rules after optimisation.3) Proposing a decision path integrator modeling method for the target region of decision paths for experimental validation. In this study, 293 patients from the BraTS2020 dataset are used as research data, and the accuracy of the fitted model is obtained to be 0.92, which is basically the same as that of Random Forest, but the model in this study has a better and simpler internal structure. At the same time, this study validated the relationship between Flair sequence and the edema region of glioma, and the experimental results showed that our extracted decision paths have a certain auxiliary effect on the segmentation of U-Net, and also proved the effectiveness of our proposed interpretability method.

关键词： Accuracy Correlation Magnetic resonance imaging Fitting Evolutionary computation data models Natural language processing Logic Random forests

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TSARNet :Integrated spatio-temporal attention mechanism for multivariate time series prediction deep learning framework

TSARNet :Integrated spatio-temporal attention mechanism for ...

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Natural Language processing (ICNLP), International Conference on

作者： Xiaoying Pan Hao Wang Jia Sun Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Multivariate time series forecasting is particularly critical in a number of areas involving air forecasting, electricity consumption and exchange rate transformations. The time series data in these fields often contain present multivariate nature, fusing long and short-term patterns, and traditional forecasting methods are often difficult to work. To address this challenge, this paper proposes a new deep learning framework, TSARNet, which combines gated recurrent units (GRUs) and an adaptive scale processing layer to effectively capture short-term localized dependency patterns among multivariate variables and reveal long-term trends through a spatio-temporal attention mechanism. In addition, an autoregressive model is incorporated to enhance the network's sensitivity to time series scale changes. Comprehensive experiments on four publicly available datasets show that the proposed TSARNet algorithm outperforms other comparative methods in most cases in terms of mean absolute error (MAE) and root mean square error (RMSE) in the long term prediction of different datasets. The TSARNet method also achieves optimal results for doing long-term prediction for each dimension of the power transformer temperature dataset.

关键词： Deep learning Attention mechanisms Accuracy Sensitivity Time series analysis Predictive models Market research Prediction algorithms Forecasting Root mean square

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