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IEEE International Symposium on Biomedical Imaging

作者： Nina Cheng Zhengji Liu Yash Deo Haoran Dou Ning Bi Kun Wu Fengming Lin Zeike A Taylor Nishant Ravikumar Alejandro F Frangi CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Leeds School of Optometry The Hong Kong Polytechnic University NIHR Leeds Biomedical Research Centre Leeds UK Alan Turing Institute London UK Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester UK Cardiovascular Sciences Departments Medical Imaging Research Center (MIRC) Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

We propose a novel pipeline for the generation of synthetic full spatial cine cardiac magnetic resonance (CMR) images via a latent Denoising Diffusion Implicit Models (DDIMs). These synthetic images can be used as viable alternatives to real data in deep learning model training for downstream cardiac image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic CMR images along with their corresponding segmentation masks. We evaluated model performance using a variety of methods, including generated image fidelity, diversity and calculated the volumes of the generated segmentation masks and compare it with the real segmentation masks. The proposed pipeline has the potential to be widely applied to other tasks in various medical imaging modalities. Effective and efficient generation of 3D cine cardiac images with corresponding segmentation masks can supplement real patient datasets and help reduce the burden of manually annotating images.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Biological system modeling Pipelines Noise reduction

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Conceptualization and iterative human-centered design of pharmacogenomic order checks

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2023年第1期12卷 31 - 34页

作者： Margo Kabel Tim Arnold Jennifer Chapman Deepak Voora Jill Bates Human Factors Engineering Clinical Informatics and Data Management Office of Health Informatics Veterans Health Administration Washington DC USA Human Factors Engineering Clinical Informatics and Data Management Office of Health Informatics Veterans Health Administration Washington DC USAUniversity of Michigan College of Pharmacy Ann Arbor USA Durham VA Health Care System Durham NC USAInstitute for Medical Research Inc. Durham NC USA Durham VA Health Care System Durham NC USA Durham VA Health Care System Durham NC USAUNC Eshelman School of Pharmacy Division of Practice Advancement and Clinical Education Chapel Hill NC USA

The usability of healthcare information systems is vital to providing safe, effective, and efficient patient care. In this paper, we describe human centered design of pharmacogenomic (PGx) clinical decision support (CDS) systems. Operationalizing PGx-based care is a relatively new endeavor, and the complexity of this clinical space requires adapting human factors techniques to fashion usable designs. We had to consider multiple design tradeoffs including whether to group medications by drug class. In this article, we provide additional design tradeoff considerations and lessons learned. Including embedded human factors practices early and throughout as a key consideration for maturing a learning health system.

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Resting-state brain network analysis and applied evaluation of global developmental delay in preterm infants: A functional near-infrared spectroscopic study

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Biosensors and Bioelectronics: X 2024年 21卷

作者： Huang, Hengye Sun, Karol Kexin Wu, Dan Zhang, Chuncao Huo, Yanyan Tian, Yuan Yu, Guangjun School of Public Health Shanghai Jiao Tong University School of Medicine Shanghai200025 China Department of Biomedical Engineering Whiting School of Engineering Johns Hopkins University BaltimoreMD21218 United States Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Center for Biomedical Informatics Shanghai Children's Hospital Shanghai Jiao Tong University School of Medicine Shanghai200040 China Department of Health Management Shanghai Children's Hospital Shanghai Jiao Tong University School of Medicine Shanghai200040 China Child Health Care Medical Division Shanghai Children's Hospital Shanghai Jiao Tong University School of Medicine Shanghai200040 China School of Medicine The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Global developmental delay (GDD) is a common neurodevelopmental disorder in children under 5 years of age. This study assessed the potential of functional Near-Infrared Spectroscopy (fNIRS) technology aiding diagnosis of GDD in premature infants and explored brain pathogenesis of GDD. This prospective cohort study was conducted between December 2020 and June 2023 among preterm infants in five hospitals in China. The primary outcome was the diagnosis of GDD. A 5-min fNIRS brain scan was performed in preterm infants at 4-, 8-, and 12-month corrected age in two hospitals. A diagnostic model based on fNIRS brain network and an integrated diagnostic model built on fNIRS data as well as birth head circumference was developed. Diagnostic models were assessed using the area under the curve (AUC) from receiver operating characteristic curves. Brain network analysis revealed significantly worse left-sided functional connectivity in GDD preterm infants. The fNIRS technique had satisfactory diagnostic value for GDD in all age groups. The diagnostic accuracy of the combined diagnostic model significantly improved. This study suggested that fNIRS could be considered for early screening and late auxiliary diagnosis of GDD in preterm infants. Furthermore, it can also be used to explore GDD brain pathogenesis. © 2024

关键词： Near infrared spectroscopy

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HNLS: Designing a Robust Model for Human Action Recognition Based on Postures by Using Hybrid Neural Learning Strategy

HNLS: Designing a Robust Model for Human Action Recognition ...

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Emerging Trends in engineering and Medical Sciences (ICETEMS), International Conference on

作者： Govindaraj Ramkumar Hasina Nasrin M Ramya Jayant Giri N P G Bhavani Ahmad Abdelhafiz Ali Samhan Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of CSE (Data Science) MLR Institute of Technology Hyderabad Telengana India Department of ECE S.A. Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagput India Division of Research and Development Lovely Professional University Phagwara India Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Human action recognition is a pivotal area in computer vision with numerous applications, such as video surveillance, healthcare monitoring, sports analysis, and human-computer interaction. This paper presents a robust methodology, Hybrid Neural Learning Strategy, which integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for effective human action recognition. The proposed approach utilizes the UCF101 dataset, which captures 101 diverse human actions with posture-specific information under various lighting, angles, and environmental conditions. The data preprocessing steps include cleaning, feature extraction using OpenPose, and dimensionality reduction through AutoEncoders. A hybrid architecture combining CNN for spatial feature extraction and LSTM for temporal dependency modeling is employed. The model achieves an accuracy of 93.78%, outperforming traditional and hybrid models, such as CNN, RNN, and AutoEncoder-LSTM models. These results demonstrate the model's efficacy in recognizing complex human actions across various scenarios, making it highly suitable for applications in surveillance, healthcare, and human-computer interaction.

关键词： Human computer interaction Autoencoders Medical services Feature extraction Video surveillance Market research Human activity recognition Convolutional neural networks Long short term memory Sports

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Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature

arXiv

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arXiv 2023年

作者： Deo, Yash Dou, Haoran Ravikumar, Nishant Frangi, Alejandro F. Lassila, Toni School of Computing and School of Medicine University of Leeds Leeds United Kingdom Leeds United Kingdom Alan Turing Institute London United Kingdom Electrical Engineering and Cardiovascular Sciences Departments Ku Leuven Leuven Belgium Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom

The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of the CoW is paramount to advance research on cerebrovascular diseases and refine clinical interventions. However, comprehensive investigation of less prevalent CoW variations remains challenging because of the dominance of a few commonly occurring configurations. We propose a novel generative approach utilising a conditional latent diffusion model with shape and anatomical guidance to generate realistic 3D CoW segmentations, including different phenotypical variations. Our conditional latent diffusion model incorporates shape guidance to better preserve vessel continuity and demonstrates superior performance when compared to alternative generative models, including conditional variants of 3D GAN and 3D VAE. We observed that our model generated CoW variants that are more realistic and demonstrate higher visual fidelity than competing approaches with an FID score 53% better than the best-performing GAN-based model. © 2023, CC BY.

关键词： Diffusion

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StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy

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Computers in Biology and Medicine 2025年 189卷 109958-109958页

作者： Raihan, Fazla Rabby Abdulrazak, Lway Faisal Rahman, Md. Ashikur Ali, Md Mamun Ibrahim, Sobhy M. Ahmed, Kawsar Bui, Francis M. Mahmud, Imran Department of Software Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Division of Biomedical Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to predict THPs became popular very rapidly, since laboratory methods are slow and resourceful. Herein, we are proposing StackTHP, a newly developed stacking-ensemble model aimed at further improving THP prediction accuracy. StackTHP implements multiple feature extraction methods, including amino acid composition (AAC), and pseudo amino acid composition (PAAC) together with classical machine learning classifiers like Extra Trees, Random Forest, and AdaBoost, while the logistic regression-based meta-classifier is used for the stacking framework. StackTHP outperformed all other models, producing an accuracy of 91.92 %, Matthew's correlation coefficient (MCC) of 0.8415, AUC of 0.977 on benchmark datasets, indicates that it is better than approaches attempted earlier and provides a robust solution for proceeding towards the discovery and development of peptide-based cancer therapies. Future research will focus on the application of StackTHP over more diverse sets of data along with some hybrid methods to enhance the prediction capability. The dataset and the code are available at the following link: https://***/Ashikur562/StackTHP. © 2025 The Authors

关键词： Prediction models

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Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

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engineering 2022年第1期8卷 116-121页

作者： Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch.Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Luxembourg Centre for Systems Biomedicine University of LuxembourgBelval L-4367Luxembourg Department of Plant Sciences University of CambridgeCambridge CB21TNUK Department of Emergency Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China AMITA Health Saint Joseph Hospital Chicago ChicagoIL 60657USA Department of Anesthesiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Huazhong University of Science and Technology-Wuxi Research Institute Wuxi 214174China Division of Cardiology Department of Internal MedicineTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Institute of Health Informatics University College LondonLondon NW12DAUK Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Infectious Diseases Shenzhen Key Laboratory of Pathogenic Microbiology and ImmunologyNational Clinical Research Center for Infectious DiseaseThe Third People’s Hospital of Shenzhen(Second Hospital Affiliated with the Southern University of Science and Technology)Shenzhen 518055China Department of Electrical and Computer Engineering&Division of Systems Engineering&Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Coronavirus disease 2019(COVID-19)has become a worldwide *** patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk ***,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of *** risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all ***,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of *** summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

关键词： COVID-19 Risk score Mortality risk prediction

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion

arXiv

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arXiv 2022年

作者： Liao, Liang Chen, Wenyi Xiao, Jing Wang, Zheng Lin, Chia-Wen Satoh, Shin'ichi The Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan The School of Computer Science Wuhan University Wuhan430072 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Understanding foggy image sequence in driving scene is critical for autonomous driving, but it remains a challenging task due to the difficulty in collecting and annotating real-world images of adverse weather. Recently, self-training strategy has been considered as a powerful solution for unsupervised domain adaptation, which iteratively adapts the model from the source domain to the target domain by generating target pseudo labels and re-training the model. However, the selection of confident pseudo labels inevitably suffers from the conflict between sparsity and accuracy, both of which will lead to suboptimal models. To tackle this problem, we exploit the characteristics of the foggy image sequence of driving scenes to densify the confident pseudo labels. Specifically, based on the two discoveries of local spatial similarity and adjacent temporal correspondence of the sequential image data, we propose a novel Target-Domain driven pseudo label Diffusion (TDo-Dif) scheme. It employs superpixels and optical flows to identify the spatial similarity and temporal correspondence, respectively, and then diffuses the confident but sparse pseudo labels within a superpixel or a temporal corresponding pair linked by the flow. Moreover, to ensure the feature similarity of the diffused pixels, we introduce local spatial similarity loss and temporal contrastive loss in the model re-training stage. Experimental results show that our TDo-Dif scheme helps the adaptive model achieve 51.92% and 53.84% mean intersection-over-union (mIoU) on two publicly available natural foggy datasets (Foggy Zurich and Foggy Driving), which exceeds the state-of-the-art unsupervised domain adaptive semantic segmentation methods. The proposed method can also be applied to non-sequential images in the target domain by considering only spatial similarity. Models and data can be found at https://***/velor2012/TDo-Dif. Copyright © 2022, The Authors. All rights reserved.

关键词： Diffusion

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The joint effects of physical activity and sleep duration on risk of osteoporosis in Taiwanese adult population: The Taiwan Biobank Study

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Osteoporosis International 2024年第3期35卷 523-531页

作者： Chen, Kun-Hui Su, Chen-Ming Liu, Wei-Ju Tzeng, Huey-En Lee, Chia-Lin Tsai, Chun-Hao Department of Orthopedic Surgery Taichung Veterans General Hospital Taichung 407219 Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung 402204 Taiwan Department of Computer Science & Information Engineering College of Computing and Informatics Providence University Taichung City 43301 Taiwan Department of Sports Medicine China Medical University Taichung 406040 Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung 407219 Taiwan Intelligent Data Mining Laboratory Department of Medical Research Taichung Veterans General Hospital Taichung 407219 Taiwan School of Medicine Taipei Medical University Taipei 11031 Taiwan Division of Transfusion Medicine /Hematology - Oncology Taichung Veterans General Hospital Taichung 40705 Taiwan Department of Public Health College of Public Health China Medical University Taichung 406040 Taiwan Department of Medicine School of Medicine National Yang Ming Chiao Tung University Taipei 112304 Taiwan Department of Orthopedics China Medical University Hospital Taichung 406040 Taiwan College of Biomedical Engineering China Medical University Taichung 406040 Taiwan

Summary: Most studies investigating the association between physical activity and osteoporosis prevention only focused on specific types of physical activity. This study’s evidence regarding the combined effects or interaction of sleep duration and physical activity. The findings emphasize the role of sleep duration and physical activity in association with osteoporosis. Purpose: The associations between physical activity, sleep duration, and prevalent osteoporosis in Taiwanese adults were studied in this cross-sectional study. Methods: The Taiwan Biobank enrolled a community-based cohort of ~ 120,000 volunteers (as of April 30, 2020) between 30 and 76 years of age with no history of cancer. Amongst, bone mineral density (BMD) measures by dual-energy X-ray absorptiometry (DXA) were available in 22,402 participants. After excluding individuals who had no complete data of BMI (n = 23), MET score (n = 207), T-score (n = 8,826), and sleep duration (n = 16), 13,330 subjects were included as the primary cohort. Univariate and multivariable regression analyses were performed to determine the associations between the presence of osteoporosis, physical activity level, sleep duration, and other variables. Results: The results showed that after adjustment, subjects with physical activity < 20 METs/week and ≥ 20 METs/week (aOR = 1.017 and 0.767, respectively) were associated with risk of osteoporosis than those with zero MET. The odds of osteoporosis were not significantly lower in subjects who slept for ≥ 8 h/day (aOR = 0.934,p=0.266). In addition, compared to short sleepers with no physical activity, adults with increased physical activity ≥ 20 METs/week and sleep ≥ 8 h/day had a significantly lowest likelihood of osteoporosis (aOR = 0.702). Those with medium physical activity (< 20 METs/week) plus average sleep duration (6.5–8 h/day) did not have significant higher odds of osteoporosis (aOR = 1.129,p=0.151). Conclusion: The findings emphasize the joint role of sleep duration

关键词： Bone mineral density (BMD) Exercise habit Osteoporosis Sleep duration Taiwan biobank

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