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A survey on recent advancements for AI enabled radiomics in neuro-oncology 1

1st International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2019

作者： Anwar, Syed Muhammad Altaf, Tooba Rafique, Khola Raviprakash, Harish Mohy-Ud-din, Hassan Bagci, Ulas Center for Research in Computer Vision University of Central Florida OrlandoFL32816 United States Department of Computer Science University of Wah Wah Pakistan Department of Software Engineering University of Engineering and Technology Taxila47050 Pakistan Lahore Pakistan

ISBN: (数字)9783030401245

ISBN: (纸本)9783030401238

Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistance in diagnosis of cancer, planning of treatment strategy, and prediction of survival. Radiomics in neuro-oncology has progressed significantly in the recent past. Deep learning has outperformed conventional machine learning methods in most image-based applications. Convolutional neural networks (CNNs) have seen some popularity in radiomics, since they do not require hand-crafted features and can automatically extract features during the learning process. In this regard, it is observed that CNN based radiomics could provide state-of-the-art results in neuro-oncology, similar to the recent success of such methods in a wide spectrum of medical image analysis applications. Herein we present a review of the most recent best practices and establish the future trends for AI enabled radiomics in neuro-oncology. © Springer Nature Switzerland AG 2020.

关键词： Medical imaging

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

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Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection

arXiv

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arXiv 2023年

作者： Yu, Jinyang Hamdan, Sami Sasse, Leonard Morrison, Abigail Patil, Kaustubh R. Research Center Jülich Germany Institute of Systems Neuroscience Medical Faculty Heinrich Heine University Düsseldorf Germany Research Center Jülich Germany Department of Computer Science 3 - Software Engineering RWTH Aachen University Aachen Germany Max Planck School of Cognition Stephanstrasse 1a Leipzig Germany

Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method. In this study, we empirically compared MV and k-fold CV using benchmark and real-world datasets. By employing Bayesian tests, we compared generalization estimates yielding three posterior probabilities: practical equivalence, CV superiority, and MV superiority. We also evaluated the differences in the capacity of the selected models and computational efficiency. We found that both MV and CV select models with practically equivalent generalization performance across various machine learning algorithms and the majority of benchmark datasets. MV exhibited advantages in terms of selecting simpler models and lower computational costs. However, in some cases MV selected overly simplistic models leading to underfitting and showed instability in hyperparameter selection. These limitations of MV became more evident in the evaluation of a real-world neuroscientific task of predicting sex at birth using brain functional connectivity. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital Healthcare Systems

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Qiao, Sibo Guo, Qiang Shi, Fengdong Wang, Min Zhu, Haohao Khan, Fazlullah Rodrigues, Joel J.P.C. Lyu, Zhihan School of Software Tiangong University Tianjin300387 China School of Artificial Intelligence Tiangong University Tianjin300387 China National Demonstration Center for Experimental Engineering Training Education Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China School of Control Sciences and Engineering Tiangong University Tianjin300387 China School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China Higher School of Technology Amazonas State University Amazonas Manaus69050-010 Brazil Department of Game Design Faculty of Arts Uppsala University Uppsala Sweden

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems. © 2013 IEEE All rights reserved.

关键词： Information leakage

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60 GHz mmWave Metasurface Superstrate for Gain and Bandwidth Improvement

60 GHz mmWave Metasurface Superstrate for Gain and Bandwidth...

引用

Radio Science Meeting (AT-AP-RASC), URSI Atlantic and Asia Pacific

作者： Asif Bilal Abdul Quddious Atsushi Kanno Tetsuya Kawanishi Marco A. Antoniades Stavros Iezekiel Emphasis Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Research Center for Excellence University of Cyprus Nicosia Cyprus National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Department of Electrical Computer &#x0026 Biomedical Engineering Ryerson University Toronto ON Canada

A multi-layer structure designed for the 60 GHz mm-wave frequency band is presented. The design consists of an antenna element on the substrate and a split ring resonator (SRR) metasurface on the superstrate. With the addition of the metasurface superstrate layer, an improved performance of the antenna in terms of gain and bandwidth is achieved. These enhancements are based on the negative index metamaterial (NIM) properties of the designed metasurface sheet. In this proposed design the bandwidth is improved from 3 GHz to 4.5 GHz, while the gain of the antenna is increased from 7.87 dBi to 13.87 dBi at 60 GHz.

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Design and Implementation of a 5G Security Testbed Based on Zero Trust Architecture

Design and Implementation of a 5G Security Testbed Based on ...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Mahnsuk Yoon Jihun Seo Jonghoon Lee Keuchul Cho Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea Department of The Fourth Industrial Revolution & Transport Center for Connected & Automated Driving Research Korea Transport Institute Sejong Korea Cyber Security Research Division ETRI(Electronics and Telecommunications Research Institute) Daejeon Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper presents the design and implementation of a 5G security testbed based on Zero Trust Architecture (ZTA). As 5G networks offer ultra-high speeds, low latency, and massive connectivity, ensuring their security has become a critical challenge, especially with the distributed nature and numerous connected devices. Traditional perimeter-based security models are no longer sufficient. Therefore, Zero Trust Architecture, which continuously verifies and monitors all resources and users regardless of their location, has been proposed as a solution. In this study, we propose a comprehensive security management system by integrating ZTA into a 5G testbed, focusing on real-time monitoring and dynamic policy enforcement using data collected from the Access and Mobility Management Function (AMF) and User Plane Function (UPF). The testbed's effectiveness is demonstrated through the deployment of Nokia's network equipment, allowing the collection and analysis of high-resolution traffic data, which aids in early threat detection and response. The proposed system enhances 5G network security and offers significant potential for application across various industries.

关键词： 5G mobile communication Security management Architecture Network security Real-time systems Threat assessment Zero Trust Security Low latency communication Monitoring

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Prediction of Epileptic EEG Signal Based on SECNN-LSTM

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Journal of New Media 2022年第2期4卷 73-84页

作者： Jian Qiang Wang Wei Fang Victor S.Sheng School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China Department of Computer Texas Tech UniversityLubbockTX 79409USA

Brain-computer Interface(BCI)technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real *** use this technology to capture brain waves and analyze the electroencephalograph(EEG)signal for feature *** the medical field as an example,epilepsy disease is threatening human health every *** propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases,overcoming the problem that the disease requires long time EEG monitoring and analysis by manual,which is a large workload and relatively subjective,and improving the prediction accuracy of epilepsy diseases by adding the attention mechanism *** experimental tests,the algorithm of SECNN-LSTM can effectively predict the EEG signal of epilepsy disease,and the correct recognition rate is *** experiment has some reference value for the subsequent research of EEG signals in other fields in deep learning.

关键词： EEG signal SECNN-LSTM feature analysis epilepsy

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Audio-Based Deep Learning Frameworks for Detecting COVID-19

Audio-Based Deep Learning Frameworks for Detecting COVID-19

引用

European Signal Processing Conference (EUSIPCO)

作者： Dat Ngo Lam Pham Truong Hoang Şefki Kolozali Delaram Jarchi School of Computer Science and Electronic Engineering University of Essex United Kingdom Center for Digital Safety & Security Austrian Institute of Technology Austria Solution Technology Unit Department FPT Software Company Limited Vietnam

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

This paper evaluates a wide range of audio-based deep learning frameworks applied to the breathing, cough, and speech sounds for detecting COVID-19. In general, the audio recording inputs are transformed into low-level spectrogram features, then they are fed into pre-trained deep learning models to extract high-level embedding features. Next, the dimension of these high-level embedding features are reduced before fine-tuning using Light Gradient Boosting Machine (LightGBM) as a back-end classification. Our experiments on the Second DiCOVA Challenge achieved the highest Area Under the Curve (AUC), F1 score, sensitivity score, and specificity score of 89.03%, 64.41%, 63.33%, and 95.13%, respectively. Based on these scores, our method outperforms the state-of-the-art systems, and improves the challenge baseline by 4.33%, 6.00% and 8.33% in terms of AUC, F1 score and sensitivity score, respectively.

关键词： COVID-19 Deep learning Sensitivity Neural networks Europe Feature extraction Boosting

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LORA (Long Range) and LORAWAN technology for IoT applications in Covid-19 pandemic

LORA (Long Range) and LORAWAN technology for IoT application...

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2020 International Conference on Advances in Computing, Communication and Materials, ICACCM 2020

作者： Rawat, Arvind Singh Rajendran, Jagadheswaran Ramiah, Harikrishnan Rana, Arti Collaborative Microelectronic Design Excellence Center Universiti Sains Malaysia Penang Malaysia Department of Electrical Engineering University of Malaya Kuala Lumpur Malaysia Department of Computer Science and Engineering Uttaranchal University Dehradun India

ISBN: (纸本)9781728197852

LoRa (Short for Long Range) is the fastest growing technology attracting researcher's interest now a days. Long battery life, long distance communication and low cost application specific devices are main focus of the engineers, LoRa is quite suitable for these requirements. IoT system includes four explicit sections for example sensors/devices, connectivity, processing of information and a user interface. The scope of communication has become the basic part on the majority of the IoT system, particularly in Wi-Fi and Bluetooth based IoT system. With the rise of LoRa technology, further enhancements to uses of the Internet of Things (IoT) can be figured it out. By utilizing a single receiver in the LoRa network, it can deal with numerous hubs at various areas inside the region, dissimilar to Wi-Fi-based framework which needs to have many access point to cover large area. Both LoRa and WiFi innovation has reduced total cost of IoT framework. For the wide range coverage of IoT system, LoRa is quite useful as compared to Wi-Fi and Bluetooth for short range. However, there are a few inconveniences on the speed of transmission and constraint on the size of the payload. The world when struggling with the COVID-19 pandemic, LoRa devices and the LoRaWAN protocol are playing an instrumental role in enabling public safety solutions to assist with contact tracing, ensuring healthcare regulation compliance in the workplace and addressing the needs of medical professionals. This paper presents a specialized work on LoRa/LoRaWAN innovation for IoT applications. © IEEE 2020. This article is free to access and download, along with rights for full text and data mining, re-use and analysis

关键词： Internet of things

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C3NET: End-to-end deep learning for efficient real-time visual active camera control

arXiv

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arXiv 2021年

作者： Kyrkou, Christos KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus 1 Panepistimiou Avenue Nicosia Cyprus

The need for automated real-time visual systems in applications such as smart camera surveillance, smart environments, and drones necessitates the improvement of methods for visual active monitoring and control. Traditionally, the active monitoring task has been handled through a pipeline of modules such as detection, filtering, and control. However, such methods are difficult to jointly optimize and tune their various parameters for real-time processing in resource constraint systems. In this paper a deep Convolutional Camera Controller Neural Network is proposed to go directly from visual information to camera movement in order to provide an efficient solution to the active vision problem. It is trained end-to-end without bounding box annotations to control a camera and follow multiple targets from raw pixel values. Evaluation through both a simulation framework and real experimental setup, indicate that the proposed solution is robust to varying conditions and able to achieve better monitoring performance than traditional approaches both in terms of number of targets monitored as well as in effective monitoring time. The advantage of the proposed approach is that it is computationally less demanding and can run at over 10 FPS (∼ 4× speedup) on an embedded smart camera providing a practical and affordable solution to real-time active *** Codes 65D19, 68T45, 68T07 © 2021, CC BY-NC-ND.

关键词： Cameras

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