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Deep Learning and Federated Learning in Breast Cancer Screening and Diagnosis: A Systematic Review

IEEE Access 2025年 13卷 76322-76351页

作者： Ciobotaru, Alexandru Corches, Cosmina Gota, Dan Miclea, Liviu Technical University of Cluj-Napoca Faculty of Automation and Computer Science Automation Department 400114 Romania

Breast cancer remains one of the leading causes of death among women worldwide. Early detection and diagnosis are crucial for effective treatment and improved patient outcome. In recent years, both deep learning and federated learning have demonstrated significant advancements in terms of their ability to reliably and securely diagnose breast cancer using various imaging techniques. Consequently, a thorough summary of recent advancements in the field of breast cancer screening and diagnosis using deep learning and federated learning, along with future research directions, is imperative. This study presents a comprehensive review of recent deep learning and federated learning-based methods for breast cancer screening and diagnosis from the classification, segmentation, and detection viewpoints using ultrasonography, mammography, magnetic resonance imaging, and histopathology images. In addition, novel deep learning and federated learning architectures that are used to reliably and securely diagnose breast cancer using various imaging techniques are highlighted. Finally, a discussion of the associated challenges and future research directions are presented. © 2013 IEEE.

关键词： Deep reinforcement learning

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Secure signal and image transmissions using chaotic synchronization scheme under cyber-attack in the communication channel

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Soft Computing 2025年第1期29卷 339-353页

作者： Nobakht, Shaghayegh Ahmadi, Ali-Akbar Industrial Automation Laboratory Electrical and Computer Engineering Department Kharazmi University Tehran Iran

In this paper, problem of secure message (signal and image) transmission is studied. The message is encrypted by masking it with a chaotic system state and then transmitted to receiver-side via a communication channel. It is assumed that the communication channel may be prone to a cyber-attack. The transmission system dynamics in presence of the cyber-attack in the communication channel is modeled by a singular dynamical system. Then, at the receiver-side, a novel observer is designed to recover the message together with the cyber-attack signal. Estimation of the cyber-attack is crucial for monitoring the communication channel and enable a network supervisor to mitigate the adverse consequences and make an appropriate strategy. It is assumed neither restrictions on the message nor on the cyber-attack signals. This makes the proposed scheme, the best solution in the real applications. Moreover, comparing to the existing methods which two public and private channels are required, only one communication channel is used in this paper for transmission the encrypted signal which results in a cheaper solution. Solving the optimization problem can be easily performed with the help of the existing toolboxes such as YALMIP and CVX software in MATLAB. Finally, numerical simulations for signal and image transmission are presented to justify the applicability and superior performance of the proposed scheme. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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Dynamic data hiding capacity enhancement for the Hybrid Near Maximum Histogram image steganography based on Multi-Pixel-Pair approach

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Multimedia Tools and Applications 2025年第7期84卷 3683-3699页

作者： Sondas, Adnan Erturk, Necla Bandirmali Department of Electronics and Automation Kocaeli University Kocaeli Turkey Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir Turkey

Increasing data hiding capacity and making it difficult to detect presence of any confidential data in stego images are the key objectives in contemporary image steganography research ever-improving the embedding efficiency. With regard to these crucial and challenging points, a new Multi-Pixel-Pair (MPP)-based data hiding approach is proposed in this paper. It can dynamically increase data embedding capacity of the classical Hybrid Near Maximum Histogram (HNMH) image steganography method while well-maintaining the embedding efficiency. In the proposed MPP approach, the peak value as a reference point in the histogram distribution of a cover image is obtained, and accordingly the pixel-pairs of interest where secret data is to be embedded are determined. Then, the pixels of the cover image are scanned sequentially and data hiding is performed on the relevant pixels by using the Least Significant Bit (LSB) method. In an extensive experimental study of the proposed MPP approach, it is shown that the classical HNMH data hiding capacity is dynamically improved 4.74 to 37.48 times while the Peak Signal to Noise Ratio (PSNR) decreases between 6.62 and 13.75 dB, implying both a reasonable trade-off and 7.61% enhanced embedding efficiency performance. Moreover, using the well-known cover image partitioning technique, offering significant improvements in image steganography, and the proposed MPP approach together, can further extend the data hiding capacity about 7.05%. © The Author(s) 2024.

关键词： Pixels

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Reinforcement Learning Algorithms with Graph Convolution Networks for Traffic Signal Control 8th

Reinforcement Learning Algorithms with Graph Convolution Ne...

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8th International Conference on Intelligent Transport Systems, INTSYS 2024

作者： Salmalge, Shreya Bhatnagar, Shalabh Department of Computer Science and Automation Indian Institute of Science Bengaluru India

ISBN: (纸本)9783031863691

Traffic congestion is the root cause of various social and economic problems like longer travel times, increased pollution, and fuel or energy consumption. Addressing the issue is becoming increasingly crucial with rising city traffic and limited road infrastructure. The way we change traffic signals has a significant impact on congestion in road networks. We implement reinforcement learning algorithms for controlling traffic signals adaptive to congestion in incoming roads at junctions. Road networks can be viewed as graphs with intersections as nodes and roads as edges. This motivates us to use graph convolutional networks (GCN) as function approximators in various RL algorithms applied to traffic signal control. We implement Deep Q-learning (DQN), Graph Convolutional Q-learning (GCQN), Graph Convolutional Actor-Critic (GCAC), and individual-DQN models to learn a deterministic policy for adaptive traffic signal control. We also present a comparison of the performances of these models and infer that GCQN models are better suited to work for large road networks. To the best of our knowledge, the Graph Convolutional Actor-Critic model is not used in any existing traffic signal control method. We also compare the GCQN and GCAC models against existing and state-of-the-art approaches. Experimental evaluation shows that our proposed method achieves performance levels comparable to the state-of-the-art techniques. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Travel time

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Autonomous embodied navigation task generation from natural language dialogues

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Science China(Information Sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Continuous Scatterplot and Image Moments for Time-Varying Bivariate Field Analysis of Electronic Structure Evolution

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IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3558-3568页

作者： Sharma, Mohit Masood, Talha Bin List, Nanna Holmgaard Hotz, Ingrid Natarajan, Vijay Norrköping Sweden KTH Royal Institute of Technology Department of Chemistry Stockholm Sweden Indian Institute of Science Department of Computer Science and Automation Bangalore India

Photoinduced electronic transitions are complex quantum-mechanical processes where electrons move between energy levels due to the absorption of light. This induces dynamics i.e., coupled changes in the electronic structure and nuclear geometry, that drive physical and chemical processes of importance in diverse fields ranging from photobiology and materials design to medicine. The evolving electronic structure can be characterized by two electron density fields: hole and particle natural transition orbitals (NTOs). A study of the two density fields helps understand the movement of electronic charge from one part of the molecule to another, specifically the donor and acceptor regions. Previous works in this area rely on side-by-side visual comparisons of isosurfaces, statistical approaches, or visual analysis of bivariate fields restricted to limited time instances. We propose a new method to analyze time-varying bivariate fields with a large number of instances, as pertinent to understand electronic structure changes during light-induced dynamics. Since the NTO fields depend on the nuclear geometry, the nuclear motion leads to a large number of bivariate field instances. Structures like tracking graphs have been used to analyze time-varying univariate fields. This paper presents a structured and practical approach to feature-directed visual exploration of time-varying bivariate fields using continuous scatterplots (CSPs) and image moment-based descriptors, tailored for studying the evolving electronic structure following photoexcitation. The CSP of the bivariate field at every time step is represented using an image moment vector of length 4. The collection of all image moment vector descriptors is considered as a point cloud in R4 and visualized using principal component analysis. Choosing an appropriate pair of principal components results in a representation of the point cloud as a curve on the plane. This representation supports tasks such as identifying intere

关键词： Directed graphs

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Identifying patients in need of psychological treatment with language representation models

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Multimedia Tools and Applications 2025年第1期84卷 397-418页

作者： Aygün, İrfan Kaya, Buket Kaya, Mehmet Department of Software Engineering Celal Bayar University Manisa Turkey Department of Electronics and Automation Fırat University Elazig Turkey Department of Computer Engineering Fırat University Elazig Turkey

Early diagnosis of psychological disorders is very important for patients to regain their health. Research shows that many patients do not realize that they have a psychological disorder or apply to different departments for treatment. The detection of hidden psychological disorders in patients will both increase the quality of life of patients and reduce the traffic of patients who apply to the wrong department. This study aimed to determine whether patients who consult a physician for any reason need psychological treatment. For this purpose, the relationships, and similarities between the sentences of previous psychiatric patients and the sentences of newly arrived patients were analyzed. Domain-based trained ELECTRA language model was used to detect sentence similarities semantically. In the study, the dialogues of patients with physicians in 92 different specialties were analyzed using the MedDialog dataset, which consists of online physician applications, and the DAIC-WOZ dataset. As a result of the experiments, 90.49% success was achieved for the MedDialog dataset and 89.36% for the DAIC-WOZ dataset. With the proposed model, patients in need of psychological treatment were identified and the medical departments where psychological problems were revealed the most were determined. These divisions are Neurology, Sexology, Cardiology, and Plastic Surgery, respectively. With the findings obtained, complications caused by psychological problems and types of diseases that are precursors to psychological disorders were determined. To the best of our knowledge, this article is the first study that aims to analyze all psychological illness instead of focusing on any of the psychological problems (depression, OCD, schizophrenia, etc.) and validated by electronic health records. © The Author(s) 2024.

关键词： Diseases

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Exponential Stability of Fractional-Order Fuzzy Multilayer Networks With Short Memory and Non-Instantaneous Impulses Via Intermittent Control

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1639-1649页

作者： Xu, Yao Chen, Zui Li, Wenxue Wu, Yongbao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Department of Computer Science and Technology Weihai264209 China Department of Mathematics Weihai264209 China Southeast University School of Automation Nanjing210096 China

Many practical processes in the actual world may suffer from non-negligible instantaneous state resets and then persist for a set amount of time, which can be characterized by non-instantaneous impulses. In this paper, intermittently controlled fractional-order fuzzy multilayer complex networks with short memory and non-instantaneous impulses are considered, which give rise to a new, hybrid dynamical system that offers a wide range of applications. By employing a discontinuously intermittent control scheme, the exponential stability issue of the above-mentioned networks is studied and supported by the Lyapunov method and graph theory. In the analysis of exponential stability, stabilized and destabilized non-instantaneous impulsive effects are discussed respectively. Ultimately, main results are applied in the typical model of fractional-order competitive neural networks, and illustrative numerical simulations are conducted to show the effectiveness of theoretical analysis. © 1993-2012 IEEE.

关键词： Lyapunov methods

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in Engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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