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MAAUNet:Exploration of U-shaped encoding and decoding structure for semantic segmentation of medical image

Journal of Measurement Science and Instrumentation 2022年第4期13卷 418-429页

作者： SHAO Shuo GE Hongwei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China

In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on ***,aggregate connection is introduced from the original feature aggregation at the same *** connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip ***,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature ***,the original convolution block is *** convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial *** connections are retained and the convolution block is turned into a multi-channel convolution *** model is made to extract multi-scale spatial *** experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.

关键词： U-shaped attention network structure of MAAUNet convolutional neural network encoding-decoding structure attention mechanism medical image semantic segmentation

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Anomaly detection and segmentation based on multi-student teacher network

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Journal of Measurement Science and Instrumentation 2022年第2期13卷 235-241页

作者： REN Chaoqiang LIU Dengfeng School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi 214122 China

In automated industrial inspection,it is often necessary to train models on anomaly-free images and perform anomaly detection on products,which is also an important and challenging task in computer *** student-teacher network trains students to regress the output of the teacher,and uses the difference between the output of the student network and the pre-trained teacher network to locate anomalies,which has achieved advanced results in the field of abnormal ***,it is slow to predict a picture,and no anomaly detection is performed.A multi-student teacher network is proposed,which uses multiple student networks to jointly regress the output of the teacher network,and the minimum square difference between the output of students and teachers in each dimension is selected as the difference *** information in the middle layer of the network is used to represent each area of the image and calculate the anomaly distance for anomaly segmentation,and the maximum abnormal score is used to represent the abnormal degree of the image for abnormal *** results on MVTec anomaly detection show that the algorithm predicts a picture in 0.17 s and can output anomaly detection results at the same time,with image AUROC reaching 91.1%and Pixel AUROC reaching 94.5%.On the wall tile dataset produced by taking pictures of real scenes,image AUROC reached 89.7%,and Pixel AUROC reached 89.1%.Compared with the original student-teacher network,the proposed method can quickly complete anomaly segmentation and anomaly detection tasks at the same time with better accuracy,and it also has better results in real applications.

关键词： student-teacher network anomaly detection anomaly segmentation unsupervised learning

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Contrastive learning based method for X-ray and CT registration under surgical equipment occlusion

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Computers in Biology and Medicine 2024年 180卷 108946页

作者： Wang, Xiyuan Zhang, Zhancheng Xu, Shaokang Luo, Xiaoqing Zhang, Baocheng Wu, Xiao-Jun School of Electronics and Information Engineering at University of Science and Technology Suzhou SuZhou215009 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science at Jiangnan University WuXi214122 China Department of Orthopaedics General Hospital of Central Theater Command of PLA WuHan430012 China Shanghai Jirui Maestro Surgical Technology Co ShangHai200000 China

Deep learning-based 3D/2D surgical navigation registration techniques achieved excellent results. However, these methods are limited by the occlusion of surgical equipment resulting in poor accuracy. We designed a contrastive learning method that treats occluded and unoccluded X-rays as positive samples, maximizing the similarity between the positive samples and reducing interference from occlusion. The designed registration model has Transformer's residual connection (ResTrans), which enhances the long-sequence mapping capability, combined with the contrast learning strategy, ResTrans can adaptively retrieve the valid features in the global range to ensure the performance in the case of occlusion. Further, a learning-based region of interest (RoI) fine-tuning method is designed to refine the misalignment. We conducted experiments on occluded X-rays that contained different surgical devices. The experiment results show that the mean target registration error (mTRE) of ResTrans is 3.25 mm and the running time is 1.59 s. Compared with the state-of-the-art (SOTA) 3D/2D registration methods, our method offers better performance on occluded 3D/2D registration tasks. © 2024 Elsevier Ltd

关键词： Surgical equipment

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A Many-Objective Evolutionary Algorithm Based on New Angle Penalized Distance

A Many-Objective Evolutionary Algorithm Based on New Angle P...

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Congress on Evolutionary Computation

作者： Junchao Fang Wei Fang Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

In evolutionary many-objective optimization, achieving better balance between convergence and diversity of the population is a crucial way to improve the efficiency of the algorithm. However, diversity measure may select the individuals having good diversity but degrade the convergence process to a certain extent. If the convergence measure focuses on the convergence of the individuals too much, it may lead to local convergence. The selection pressure achieves a severe loss, especially when the Pareto dominance selection mechanism is difficult to select solutions. To address these issues, a many-objective evolutionary algorithm based on new angle penalized distance is proposed in this paper, which is termed MaOEA-NAPD. In MaOEA-NAPD, it could dynamically balance the convergence and diversity of the population concerning their importance degree during the evolutionary process based on new angle penalized distance. In order to enhance the selection probability of better solutions in the mating pool, new convergence measure and diversity measure are introduced according to the achievement scalarizing function and angle based crowding degree estimation, respectively. The performance of the proposed method is evaluated and compared with five state-of-the-art algorithms on the WFG test suites with up to 15 objectives. Experimental results show the superior performance of MaOEA-NAPD than the compared algorithms on all the considered test instances.

关键词： Heuristic algorithms Sociology Estimation Evolutionary computation Statistics Optimization Convergence

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An Efficient and Flexible Automatic Search Algorithm for Convolution Network Architectures

An Efficient and Flexible Automatic Search Algorithm for Con...

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Congress on Evolutionary Computation

作者： Liang Zhao Wei Fang Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

As many superior convolutional neural networks (CNNs) have been proposed in recent years, CNNs have played an important role in computer vision. However, manually-designing CNN architecture is difficult since expertise is required. Therefore, several automatic search algorithms have been proposed for neural architecture search, which usually have considerable computational complexity and the search space is limited. To address these problems, an efficient and flexible CNN architecture search algorithm (EF-CNN) is proposed in this paper. In EF-CNN, a flexible architecture search space is constructed by considering the depth, width, and lightweight blocks. In order to improve the reliability of the architecture while reducing the computational time, a multi-objective fitness correction method is proposed in EF-CNN based on the divided datasets, where the accuracy and computational complexity of architecture are considered simultaneously to design CNN. The experimental results on CIFAR-10 and CIFAR-100 indicate that the performance of CNN architecture designed by EF-CNN is very competitive while the computational time is greatly reduced.

关键词： Computer vision Convolution Computer architecture Manuals Evolutionary computation Network architecture Search problems

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A Dual-Branch Network for Infrared and Visible Image Fusion

A Dual-Branch Network for Infrared and Visible Image Fusion

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International Conference on pattern recognition

作者： Yu Fu Xiao-Jun Wu Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

In recent years, deep learning has been used extensively in the field of image fusion. In this article, we propose a new image fusion method by designing a new structure and a new loss function for a deep learning model. Our backbone network is an autoencoder, in which the encoder has a dual branch structure. We input infrared images and visible light images to the encoder to extract detailed information and semantic information respectively. The fusion layer fuses two sets of features to get fused features. The decoder reconstructs the fusion features to obtain the fused image. We design a new loss function to reconstruct the image effectively. Experiments show that our proposed method achieves state-of-the-art performance.

关键词： Deep learning Fuses Image synthesis Semantics Feature extraction Decoding Data mining

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Convolutional Neural Network-Based Multimodal Image Information Fusion for Moisture Damage Assessment of Cultural Heritage Buildings

SSRN

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SSRN 2024年

作者： Wang, Fuzhi Huang, Jizhong Fu, Yu Department of Civil Engineering Shanghai University Shanghai200444 China Institute for the Conservation of Cultural Heritage Shanghai University Shanghai200444 China Key Laboratory of Silicate Cultural Relics Conservation Shanghai University Ministry of Education Shanghai200444 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

Water is an essential factor causing the deterioration of historical and cultural heritage, and identifying and quantifying moisture damage is essential for conserving cultural heritage buildings. This study combines infrared thermography (IRT) and microwave technology to investigate moisture-induced diseases and the variation of the spatial distribution of water content in the Yungang Grottoes of China, a World Heritage Site. Information on building humidity distribution was extracted and combined from IRT and microwave information. Furthermore, multi-feature image fusion combines different types of monitoring information through the Convolutional Neural Network (CNN), which is used to diagnose moisture distribution and content accurately. The features obtained from different non-destructive methods were then jointly analyzed using statistical analysis methods such as absolute contrast, noise ratio (SNR), and spatial stability to quantify the artifact moisture defect data, thus improving the accuracy and range of features for detecting water-damaged pathologies. © 2024, The Authors. All rights reserved.

关键词： Convolution

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A dual-branch network for infrared and visible image fusion

arXiv

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arXiv 2021年

作者： Fu, Yu Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

关键词： Image fusion

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Riemannian Self-Attention Mechanism for SPD Networks

arXiv

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arXiv 2023年

作者： Wang, Rui Wu, Xiao-Jun Li, Hui Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

Symmetric positive definite (SPD) matrix has been demonstrated to be an effective feature descriptor in many scientific areas, as it can encode spatiotemporal statistics of the data adequately on a curved Riemannian manifold, i.e., SPD manifold. Although there are many different ways to design network architectures for SPD matrix nonlinear learning, very few solutions explicitly mine the geometrical dependencies of features at different layers. Motivated by the great success of self-attention mechanism in capturing long-range relationships, an SPD manifold self-attention mechanism (SMSA) is proposed in this paper using some manifold-valued geometric operations, mainly the Riemannian metric, Riemannian mean, and Riemannian optimization. Then, an SMSA-based geometric learning module (SMSA-GLM) is designed for the sake of improving the discrimination of the generated deep structured representations. Extensive experimental results achieved on three benchmarking datasets show that our modification against the baseline network further alleviates the information degradation problem and leads to improved accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Deep Learning in Palmprint recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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