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Enhancing medical image segmentation with MA-UNet: a multi-scale attention framework

VISUAL COMpUTER 2025年第8期41卷 6103-6120页

作者： Li, Hongzhi Ren, Zhanghao Zhu, Guoqing Liang, Yaoju Cui, Han Wang, Chaozeyu Wang, Jiaxi Chengdu Univ Sch Comp Sci Chengdu Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu Peoples R China Chengdu Univ Sichuan Prov Dept Culture & Tourism Key Lab Digital Innovat Tianfu Culture Chengdu Peoples R China

Medical image segmentation is crucial for accurate diagnosis and treatment planning. Traditional methods struggle with complex medical images, while recent deep learning advancements, particularly U-Net and its variants, often suffer from insufficient feature extraction and misalignment issues. This paper introduces MA-UNet, an enhanced segmentation network based on U-Net, which integrates multi-scale features and a hybrid attention mechanism. MA-UNet is designed with a two-stage encoder comprising rough ordinary extraction and multi-scale fine extraction to improve feature representation. A hybrid feature optimization module is embedded in skip connections to address feature misalignment. Additionally, the Transformer in the bottleneck layer is optimized, and multi-scale convolution is utilized in the decoder. Experimental results on three datasets demonstrate that MA-UNet achieves optimal or near-optimal segmentation performance, outperforming ten state-of-the-art methods. The proposed framework significantly enhances medical image segmentation accuracy, potentially aiding clinicians in making more precise decisions. Our code is available at https://***/HZ-LL/MA-UNet.

关键词： Medical image segmentation U-Net Attention mechanism Feature fusion

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A Stage-Specific Gene Expression Framework for promoting Genetic Algorithm

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IEEE ACCESS 2023年 11卷 43805-43821页

作者： Feng, pan Su, Haozhou Guizhou Minzu Univ Coll Data Sci & Informat Engn Guiyang 550025 Guizhou Peoples R China Guizhou Minzu Univ Key Lab Pattern Recognit & Intelligent Syst Guiyang 550025 Guizhou Peoples R China

Genetic algorithm (GA) has become very popular for tackling computationally expensive numerical optimization problems. To optimize their performance and convergence rate, we propose and demonstrate an embedded stage-specific gene expression framework (SGEF) with a two-tier coding scheme and a filter operator based on variational auto-encoder. For the first time, the structure of variational auto-encoder is used to map the parent population of a genetic algorithm from the uniformly distributed high dimensions to the Gaussian distributed low dimensions. The design of the filter operator which can maintain the trade-off balance between exploitation and exploration is also analyzed in detail. The experimental results demonstrated the good performance of the proposed SGEF in promoting both single-objective optimization genetic algorithms and multiple-objective optimization genetic algorithms.

关键词： Genetic algorithms Statistics Sociology Gene expression RNA Search problems Linear programming Neural networks Genetic algorithm variational auto-encoder neural networks stage-specific gene expression theory exploration and exploitation

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AE-UNet: a composite lung CT image segmentation framework using attention mechanism and edge detection

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JOURNAL OF SUpERCOMpUTING 2025年第1期81卷 1-21页

作者： Li, Hongzhi Ren, Zhanghao Zhu, Guoqing Wang, Jiaxi Chengdu Univ Sch Comp Sci Chengdu Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu Peoples R China Chengdu Univ Sichuan Prov Dept Culture & Tourism Key Lab Digital Innovat Tianfu Culture Chengdu Peoples R China

The primary impediments in lung CT image segmentation stem from the ambiguity in edge definition and the inadequate segmentation accuracy. Addressing these issues, this paper introduces a novel composite lung CT image segmentation framework that integrates an attention mechanism with an edge detection operator. We utilize residual dynamic convolutions as the encoder to augment the network's capability for extracting and representing nuanced lesion features. Sobel edge detection is integrated into the skip connections to facilitate the transmission and utilization of edge information. In particular, we introduce an information fusion attention module for deeper layers, optimizing feature reorganization and utilization by attention mechanisms and dilated convolution. Experimental evaluations on two lung CT datasets reveal that our proposed AE-UNet achieves outstanding segmentation performance, surpassing the best baseline network by an average of 0.93%.

关键词： CT image segmentation UNet Sobel operator Attention mechanism

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Small-convection limit for two-dimensional chemotaxis-Navier-Stokes system with logarithmic sensitivity and logistic-type source

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BOUNDARY VALUE pROBLEMS 2022年第1期2022卷 1-17页

作者： Wu, Jie Chengdu Univ Coll Comp Chengdu 610106 Peoples R China Chengdu Univ Key Lab Pattern Recognit & Intelligent Informat P Chengdu 610106 Peoples R China

In this paper, we consider the small-convection limit of chemotaxis-Navier-Stokes system with logarithmic sensitivity and logistic-type source {n(t)(kappa) + u(kappa).del n(kappa) = Delta n(kappa)-chi del.(n(kappa)del log c(kappa)) + f(n(kappa)), x is an element of Omega, t > 0, c(t)(kappa)+u(kappa).del c(kappa) = Delta c(kappa)-c(kappa) + n(kappa), x is an element of Omega, t > 0, u(t)(kappa)+kappa(u(kappa).del)u(kappa) = Delta u(kappa) + del p-kappa+n(kappa)del phi, x is an element of Omega, t > 0, del.u(kappa) = 0, x is an element of Omega, t > 0, in a bounded convex domain Omega subset of R-2 with smooth boundary, where kappa is an element of R, f(s) = mu(1)s - mu(2)s(lambda), lambda > 1, and : phi: Omega -> R is a given smooth potential with second-order partial derivatives. When the chemotaxis sensitivity chi satisfies the appropriate conditions, it is proved that the unique global classical solutions (n(kappa), c(kappa), u(kappa)) will stabilize to (n(0), c(0), u(0)) as kappa -> 0.

关键词： Chemotaxis-Navier-Stokes Logarithmic sensitivity Logistic-type source Small-convection limit Convergence

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Generating a 30 m Hourly Land Surface Temperatures Based on Spatial Fusion Model and Machine Learning Algorithm

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SENSORS 2024年第23期24卷 7424页

作者： Su, Qin Yao, Yuan Chen, Cheng Chen, Bo Chengdu Univ Sch Architecture & Civil Engn Chengdu 610106 Peoples R China Chengdu Univ Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu 610106 Peoples R China Chengdu Univ technol Coll earth Sci Chengdu 610059 Peoples R China

Land surface temperature (LST) is a critical parameter for understanding climate change and maintaining hydrological balance across local and global scales. However, existing satellite LST products face trade-offs between spatial and temporal resolutions, making it challenging to provide all-weather LST with high spatiotemporal resolution. In this study, focusing on Chengdu city, a framework combining a spatiotemporal fusion model and machine learning algorithm was proposed and applied to retrieve hourly high spatial resolution LST data from Chinese geostationary weather satellite data and multi-scale polar-orbiting satellite observations. The predicted 30 m hourly LST values were evaluated against in situ LST measurements and Sentinel-3 SLSTR data on 11 August 2019 and 21 April 2022, respectively. The results demonstrate that validation based on the in situ LST, the root mean squared error (RMSE) of the predicted LST using the proposed framework are around 0.89 degrees C to 1.23 degrees C. The predicted LST is highly consistent with the Sentinel-3 SLSTR data, and the RMSE varies from 0.95 degrees C to 1.25 degrees C. In addition, the proposed framework was applied to Xi'an City, and the final validation results indicate that the method is accurate to within about 1.33 degrees C. The generated 30 m hourly LST can provide important data with fine spatial resolution for urban thermal environment monitoring.

关键词： land surface temperature Chinese geostationary weather satellite multi-scale polar-orbiting satellite spatiotemporal fusion model

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Modeling Rumor Unidirectional Spreading from Online Social Networks to Offline 7

Modeling Rumor Unidirectional Spreading from Online Social N...

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7th International Conference on Electronic information Technology and Computer Engineering (EITCE)

作者： Han, Qiyi Yi, Chen Chengdu Univ Sch Comp Sci Key Lab Pattern Recognit & Intelligent Informat P Chengdu Sichuan Peoples R China Chengdu Univ Informat Technol Coll Elect Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9798400708305

Online social networks not only facilitate the dissemination of information, but also increase the risk of rumors. This paper focuses on studying the unidirectional spread of rumors from online social networks to offline environments. To describe the dynamic process of rumor spreading, we derive a unidirectional coupled network structure and mean-field equations. We illustrate the performance of rumor spreading under various scenarios using computer simulations. The simulations reveal that rumors in unidirectional coupled networks spread faster and wider than those in single layer networks. Furthermore, with the assistance of unidirectional links, rumors tend to persist for a longer duration and cause more severe damages.

关键词： Online social network (OSN) Rumor spread Unidirectional coupled networks SIR model

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Bifurcation analysis, stationary optical solitons and exact solutions for generalized nonlinear Schrodinger equation with nonlinear chromatic dispersion and quintuple power-law of refractive index in optical fibers

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pHYSICA A-STATISTICAL MECHANICS AND ITS AppLICATIONS 2023年 615卷

作者： Han, Tianyong Li, Zhao Li, Chenyu Chengdu Univ Coll Comp Sci Chengdu 610106 Peoples R China Sichuan Normal Univ VC & VR Key Lab Sichuan Prov Chengdu 610068 Peoples R China Chengdu Univ Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu 610106 Peoples R China

In this paper, the bifurcation, stationary optical solitons and exact solutions in optical fiber propagation are studied. The propagation model is described by a generalized non-linear Schrodinger equation with Kudryashov's quintuple power law of refractive index. First, the equation is transformed into a second-order nonlinear ordinary differential equation by an appropriate transformation. Then, the bifurcation of the phase diagram of the obtained ordinary differential equation is established using planar dynamic system theory, and the types of optical solitons and exact solutions are determined. What is more, the stationary optical solitons and exact solutions are obtained by the polynomial complete discriminant system, and are demonstrated in 3D and 2D graphics by using the symbolic computation software Maple. Finally, the obtained stationary optical solitons and exact solutions include the Jacobi elliptic function solution, hyperbolic function solution, rational function solution, trigonometric function solution and exponential function solution. Moreover, the results provide a method to further study stationary optical solitons and exact solutions of propagation pulses in optical fibers.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Stationary optical soliton Exact solution Bifurcation Nonlinear chromatic dispersion Quintuple power-law of refractive index

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Mind the Gap: Open Set Domain Adaptation via Mutual-to-Separate Framework

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第6期34卷 4159-4174页

作者： Chang, Dongliang Sain, Aneeshan Ma, Zhanyu Song, Yi-Zhe Wang, Ruiping Guo, Jun Beijing Univ Posts & Telecommun Sch Artificial Intelligence Pattern Recognit & Intelligent Syst Lab Beijing 100876 Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH England Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China

Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled target domain. Amongst its many variants, open set domain adaptation (OSDA) is perhaps the most challenging one, as it further assumes the presence of unknown classes in the target domain. In this paper, we study OSDA with a particular focus on enriching its ability to traverse across larger domain gaps, and we show that existing state-of-the-art methods suffer a considerable performance drop in the presence of larger domain gaps, especially on a new dataset (pACS) that we re-purposed for OSDA. Exploring this is pivotal for OSDA as with increasing domain shift, identifying unknown samples in the target domain becomes harder for the model, thus making negative transfer between source and target domains more challenging. Accordingly, we propose a Mutual-to-Separate (MTS) framework to address the larger domain gaps. Essentially we design two networks - (a) Sample Separation Network (SSN): which is trained to learn a hyperplane for separating unknown samples from known ones, and (b) Distribution Matching Network (DMN): which is trained to maximise domain confusion between source and target domains without unknown samples under the guidance of the SSN. The key insight lies in how we exploit the mutually beneficial information between these two networks. On closer observation, we see that SSN can reveal which samples in the target domain belong to the unknown class by instance weighting whereas, DMN pushes apart the samples that most likely belong to the unknown class in the target domain, which in turn reduces the difficulty of SSN in identifying unknown samples. It follows that (a) and (b) will mutually supervise each other and alternate until convergence, which can better align the source and target domains in the shared label space. Extensive experiments on five datasets (Office-31, Office-Home, pACS, VisDA, and mini _DomainNet) demonstrate the effi

关键词： picture archiving and communication systems Training Task analysis labeling Adaptation models Visualization information exchange Domain adaptation open set mutual learning transfer learning

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Forgetting memristor based STDp learning circuit for neural networks

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NEURAL NETWORKS 2023年 158卷 293-304页

作者： Zhou, Wenhao Wen, Shiping Liu, Yi Liu, Lu Liu, Xin Chen, Ling Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligent Elect Informat & Engn Chongqing 400715 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ctr Artificial Intelligence Ultimo Australia Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Comp Vis & Pattern Recognit Lab Lappeenranta Finland

The circuit implementation of STDp based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDp is still rare. This paper proposes a new STDp learning rule implementation circuit based on the forgetting memristor. This kind of forgetting memory resistance synapse makes the neural network have the function of time-division multiplexing, but the instability of short-term memory will affect the learning ability of the neural network. This paper analyzes and discusses the influence of synapses with long-term and short-term memory on the learning characteristics of neural network STDp, which lays a foundation for the construction of time-division multiplexing neural network with long-term and short-term memory synapses. Through this circuit, it is found that the volatile memristor has different behaviors to the stimulus signal in different initial states, and the resulting LTp phenomenon is more in line with the forgetting effect in biology. This circuit has multiple adjustable parameters, which can fit the STDp learning rules under different conditions. The application of neural network proves the availability of this circuit.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Forgetting memristor Spike timing dependent plasticity (STDp) Learning rule Circuit Neural networks

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A Deep Learning Inversion Method for Airborne Time-Domain Electromagnetic Data Using Convolutional Neural Network

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AppLIED SCIENCES-BASEL 2024年第19期14卷 8883页

作者： Yu, Xiaodong Zhang, peng Yu, Xi Chengdu Univ Coll Comp Sci Chengdu 610106 Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu 610106 Peoples R China Chengdu Univ Technol Coll Comp Sci & Cyber Secur Chengdu 610059 Peoples R China

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data;the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods.

关键词： deep learning fast inversion airborne time-domain electromagnetic convolutional neural network

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