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Multimodal Brain Tumor Segmentation Using Modified 3D UNet3+ Architecture 1

Challenge on Brain Tumor Segmentation, BraTS 2023, International Challenge on Cross-Modality Domain Adaptation for Medical image Segmentation, CrossMoDA 2023, held in conjunction with the Medical image Computing for Computer Assisted Intervention Conference, MICCAI 2023

作者： Yang, Xiao Zheng, Shaohua Intelligent Image Processing and Analysis Laboratory Fuzhou University Fuzhou China

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Glioma is a type of tumor that originates in the glial cells of the brain or the spinal cord, which is the most common and aggressive malignant primary tumor of the central nervous system in adults. Magnetic resonance imaging (MRI) is an efficient way of detection for brain tumors, it contains precise knowledge about the location of the tumor and its component. Manual tumor labeling is a tedious and time-consuming task, hence accurate automatic segmentation has important clinical significance in the diagnosis, and deep learning shows great power on it. In this paper, we propose an improved 3D U-Net3 + segmentation network. We adopt the residual structure in the encoder part to address the challenge of diminishing gradient problem, as well as to enhance the encoder’s capability for feature extraction, thereby facilitating comprehensive feature fusion during up-sampling within the network. Meanwhile, we add a critic network at the end of the U-Net3+, that contributes to the generation of segmentation results that are more authentic and dependable from the segmentation network. We trained and evaluated the architecture on the BraTS 2023 dataset, and achieved LesionWise_Dice of 88.22%, 83.85%, 81.97% for the whole tumor, tumor core and enhancing tumor, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

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Controllability of neighborhood Corona product networks

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Science China(Information Sciences) 2024年第12期67卷 327-328页

作者： Bo LIU Xuan LI Qiang ZHANG Junjie HUANG Housheng SU Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages School of Information Engineering Minzu University of China School of Mathematical Sciences Inner Mongolia University Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology

Controlling networks aims to study the models, structures,and related dynamics of complex networks. The primary problem of controlling networks is to determine whether they are controllable. Nowadays, controllability has been widely studied and applied to system engineering and control theory, power systems, aerospace, and quantum systems. Various classical criteria include the Gram matrix criterion, Kalman rank criterion, and PBH test.

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RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

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Computers, Materials & Continua 2024年第5期79卷 2879-2905页

作者： Xiabin Zhang Zhongyi Hu Lei Xiao Hui Huang College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Key Laboratory of Intelligence Image Processing and Analysis Wenzhou UniversityWenzhou325035China

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global *** contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel ***,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is *** tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features *** combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification ***,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer ***,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC *** demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic ***,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet ***,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

关键词： Alzheimer CNN structural reparameterization multi head self attention computer aided diagnosis

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Attention-stacking adaptive fuzzy neural networks for autism diagnosis

Attention-stacking adaptive fuzzy neural networks for autism...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Shuzhan Xiao, Lei Huang, Hui Hu, Zhongyi Wenzhou University Institute of Intelligent Information System Wenzhou University Key Laboratory of Intelligent Image Processing and Analysis College of Computer Science and Artificial Intelligence Wenzhou China

ISBN: (纸本)9798350386226

The current diagnostic methods for Autism Spectrum Disorder (ASD) based on Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) face two significant challenges. Firstly, the functional connectivity networks (FCNs) of the brain are subject to uncertainty due to noise interference during the rs-fMRI acquisition process. Secondly, variations in equipment parameters across different imaging sites lead to diverse distributions of collected features. To address these issues, this paper introduces a novel Multi-Task Stacked Adaptive Fuzzy Neural Network (AT-STTSK) specifically designed for Autism Spectrum Disorder (ASD) classification. The proposed network seamlessly integrates a Multi-Output TSK (MO-TSK) fuzzy system, a Stacked Autoencoder (SAE), and a Transformer. This integration aims to reduce the impact of uncertainty factors, enhance the model's interpretability, and leverage the multi-head attention mechanisms to capture both local and global information, thus strengthening feature learning. Additionally, we explore the utilization of a Contrastive Learning (CL) strategy to further optimize the model. By incorporating these advancements, our AT-STTSK network offers a promising approach for improving the accuracy and reliability of ASD diagnosis based on rs-fMRI data. The validity of the method in this paper is verified on ABIDE dataset, and the result shows that the accuracy rate can reach 85.7% at most. © 2024 IEEE.

关键词： Diseases

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Short-Term Residential Load Forecasting via Pooling-Ensemble Model with Smoothing Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3690-3702页

作者： Xiao, Jiang-Wen Fang, Hongliang Wang, Yan-Wu Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this article proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole short-term load forecasting framework in this article contains three steps. Specifically and first, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Second, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Third, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2020 IEEE.

关键词： Probability distributions

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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Underwater target recognition based on adaptive multi-feature fusion network

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Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Attitude Detection Model Based on Improved High Resolution Network 13

Attitude Detection Model Based on Improved High Resolution N...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Shi, Dongliang Li, Yuhan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

2D human posture detection, a challenging task in computer vision, aims to identify human body key points and is widely used. The HRNet model performs well in human key point detection. To enhance its effect in human posture detection and address suboptimal prediction accuracy in interactive occlusion, this paper combines HRNet and Transformer. The Transformer's attention mechanism handles complex image dependencies, and HRNet obtains image features. By embedding the Transformer's attention mechanism module into HRNet's backbone network and integrating a self-designed double-branch structure and convolutional attention module, an improved HRNet model is constructed. The DConvAttention module processes extracted features, and the convolutional attention mechanism balances image frequencies, enhancing processing and discrimination of complex backgrounds and occlusions and improving detection performance. In tests of complex and occluded images, the improved HRNet Local model has a 4.2M parameter increase, 0.5G FLOPs increase, and 1% mAP increase compared to HRNet, with the same recall rate. The improved HRNet Global model has 11M more parameters, 1.1G more FLOPs, a similar or slightly higher recall rate, and a 2% higher mAP value. © 2025 IEEE.

关键词： Face recognition

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A Novel Self-Learning Network Integrating Contrastive Learning, Perceptual Learning and Masked image Modelling 15

A Novel Self-Learning Network Integrating Contrastive Learni...

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15th International Conference on Graphics and image processing, ICGIP 2023

作者： Chen, Yingxian Yang, Rui Lan, Rushi Guangxi Key Laboratory Image and Graphic Intelligent Processing Guilin University of Electronic Technology China

ISBN: (纸本)9781510675018

Unsupervised learning methods in computer vision have achieved remarkable success, exceeding the performance of supervised learning methods. It is noteworthy that current unsupervised learning methods share certain similarities, particularly in their data augmentation techniques. Masking, a type of data augmentation, can be utilized for both contrastive learning and masked image modelling. This paper presents a novel deep learning approach on visual unsupervised learning. It integrates previous methods such as contrastive learning, perceptual learning, self-distillation and masked image modelling. In our method, we treat the network that handles the original images as the teacher network, and the network that handles the masked images as the student network. The student network employs the representations extracted by the projection head for contrastive learning, while the features generated by the decoder are employed for masked image modeling. The process of self-knowledge distillation is facilitated by perceptual learning between the teacher and student networks. This model aligns with the main idea of contrastive learning, which aims to pull similar images closer while pushing dissimilar images further apart. Simultaneously, it reflects the main idea of masked image modelling, which enables the extraction of semantic information from large scale masked pixel reconstruction tasks. Additionally, we compare the effect of self-supervised methods to the performance of the model. Our results show that with only 75 epochs of fine-tuning, our 29M-parameter model achieves 78.5% top-1 accuracy on the imageNet-1k dataset. © 2024 SPIE. All rights reserved.

关键词： Unsupervised learning

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Design of Dual-mode DPE Receiver Based on GPS L1/BDS B1C Signals

Design of Dual-mode DPE Receiver Based on GPS L1/BDS B1C Sig...

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2025 International Technical Meeting of The Institute of Navigation, ITM 2025

作者： Jia, Qiongqiong Guo, Qiqi Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin300000 China

ISBN: (纸本)9780936406404

With the expanding applications of the Global Navigation Satellite System (GNSS), its operating environment has become increasingly complex, with challenges such as multipath effects, non-line-of-sight (NLOS) propagation, and signal attenuation caused by tall urban buildings and trees. This complexity poses additional challenges for traditional GNSS receivers. Direct Position Estimation (DPE) addresses some of the limitations of traditional receivers by accumulating signals from all visible satellites directly in the navigation domain. However, when severe obstructions result in a reduced number of visible satellites, DPE may fail to function properly. The introduction of multiconstellation satellite signals can help mitigate the issue of insufficient visible satellites in complex environments. Based on this, this paper designs a dual-mode DPE receiver utilizing Global Positioning System (GPS) L1 signals and BeiDou Navigation Satellite System (BDS) B1C signals. © 2025 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM. All rights reserved.

关键词： Global positioning system

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