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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Teeth Segmentation from Bite-Wing X-Ray Images by Integrating Nested Dual UNet with Swin Transformers

Teeth Segmentation from Bite-Wing X-Ray Images by Integratin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yubo Li Yibin Tian Zhiyuan Zhang Xueyang Zhang Bingran Du Zhi Zeng School of Computer and Information Science Chongqing Normal University Chongqing CN College of Mechatronics and Control Engineering Shenzhen University Shenzhen CN Litemaze Technology Shenzhen CN School of Computing and Information Systems Singapore Management University Singapore SG Department of Stomatology Shunde Hospital Southern Medical University (The First People's Hospital of Shunde) Foshan CN

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In medical practice, the precision of image segmentation is crucial for diagnosis and treatment evaluations. Specifically, in dentistry, accurate teeth segmentation from bite-wing images is important for automatic and objective evaluations of root canal treatments. This study introduces $\text{Swin}-\mathrm{U}^{2} \text{Net}$ , a model merging the nested dual UNet with residual U-block and Swin Transformers. It combines the local feature extraction capability of the former and the global attention and context understanding of the latter. It has been evaluated for tooth root segmentation using 500 bite-wing dental x-ray images obtained from a root canal treatment clinic. It achieved the best segmentation outcome in terms of Intersection over Union (IOU) and the third best result in terms of Dice Similarity Coefficient (DSC) with the second least amount of network parameters among six UNet-like models, thus it is effective and efficient.

关键词： Image segmentation Irrigation Accuracy Merging Teeth Transformers Feature extraction Dentistry X-ray imaging Medical diagnostic imaging

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Outcomes of M-health implementation on health workers' transformation in child health care service delivery: A case of ALMANACH 24

Outcomes of M-health implementation on health workers' trans...

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24th Pacific Asia Conference on information Systems: information Systems (IS) for the Future, PACIS 2020

作者： David, Nakama Kah, Muhammadou Tyndall, Jennifer Longe, Olumide School of Information Technology and Computing American University of Nigeria Nigeria School of Arts and Science American University of Nigeria Nigeria

The child mortality rate remains significantly high in many developing countries despite recent contemporary approaches to reducing child mortality rates across the globe. This is especially the case for armed-conflict settings such as Syria, Nigeria, and Niger where approximately 6 million children under the age of five died in 2015. Mhealth is considered a promising information and communication technologies in improving child health outcomes in such a peculiar setting with severe health care deficit. This study is concerned with the analysis of the outcomes of implementing mHealth for health workers' transformation in child health care. To illuminate such an investigation, the study employs the lens of the Cultural-Historical Activity Theory (CHAT). Concepts from CHAT are elaborated to give a deep underpinning of the implementation of ALAMANCH as an alternative model of activity in the child health care activity system. An action research case study approach was used to investigate this study. Observations and interviews were used as the drivers for data collection with semi-structured interviews as the main source of data collection. A total of 31 one-on-one semi-structured interviews were conducted with the health workers while a total of twelve sessions of observation were made in ten PHCs to observe pre and post ALAMANACH implementation processes. Our findings suggest that mHealth implementation has transformed the activities of health workers by creating new roles, division of labor, and new tools in the child health care service delivery system. © Proceedings of the 24th Pacific Asia Conference on information Systems: information Systems (IS) for the Future, PACIS 2020. All rights reserved.

关键词： information use

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Hierarchical features-based targeted aspect extraction from online reviews

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Intelligent Data Analysis 2021年第1期25卷 205-223页

作者： He, Jin Li, Lei Wang, Yan Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Department of Computing Macquarie University Sydney Australia MiningLamp Academy of Sciences Mininglamp Techonologies Beijing China

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification. © 2021 - IOS Press. All rights reserved.

关键词： Aspect extraction Focused analysis Online reviews Text mining Topic modeling

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Quantum Memory Error Correction Computation Based on Chamon Model

SSRN

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SSRN 2023年

作者： Zhao, Jian Wu, Yu-Chun Guo, Guo-Ping Key Laboratory of Quantum Information Chinese Academy of Sciences School of Physics University of Science and Technology of China Anhui Hefei230026 China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Anhui Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China Origin Quantum Computing Anhui Hefei230026 China

Quantum error correction codes play a central role in the realisation of fault-tolerant quantum computing. Chamon model is a 3D generalization of the toric code. The error correction computation on this model has not been explored so far. In this work, the Chamon model is turned to a non-CSS error correction code. Logical qubits are built by the construct of logical Pauli operators. The property of logical operators reveals the expressions of code distance. According to the topological properties of Chamon models, an error elimination algorithm is proposed. Based on the error elimination algorithm, we propose a global randomized error correction algorithm to decode Chamon models in every single-qubit depolarized channel. This decoding algorithm is improved by adding the pretreatment process, termed the probabilistic greedy local algorithm, which adapts to different kinds of high-dimensional models. The estimated threshold error rate for numerical experiment can be raised to 4.92%. © 2023, The Authors. All rights reserved.

关键词： Error correction

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APC: Contactless healthy sitting posture monitoring with microphone array

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Smart Health 2024年 32卷

作者： Ma, Kaiyuan Song, Shunan An, Lingling Mao, Shiwen Wang, Xuyu Guangzhou Institute of Technology Xidian University Guangzhou China School of Computer Science and Technology Xidian University Xi'an China Department of Electrical and Computer Engineering Auburn University Auburn 36849 AL United States Knight Foundation School of Computing and Information Sciences Florida International University Miami 33199 FL United States

The prevalence of poor sitting posture in daily work has become a growing concern among office workers and students due to the associated health problems. To address this issue, we design an acoustic sitting posture care system (termed, APC) based on a circular microphone array. Compared with classic posture recognition technologies such as visual perception and sensors, acoustic sensing naturally possesses advantages such as privacy protection and contactless capabilities. Concretely, our system leverages a customized and inaudible sound signal sent from a speaker to a user's body, and an echo signal preprocessing method to sense the body posture. Our system comprises three modules: signal generation and collection, signal preprocessing, and posture classification. The signal generation and collection module is designed to create an appropriate signal waveform for transmitting the sound signal. We also develop a unique alignment method for received signals to implement background interference cancellation. In the signal preprocessing module, we propose a body profile extraction method based on the phase difference between received signals. In the posture classification module, we design an attention mechanism based classification network that can map the output of the previous module to different sitting posture categories. The experimental results show that our proposed method achieves an average accuracy of 98.4% for five common sitting postures. Furthermore, case studies conducted under different practical conditions have validated the robustness of our system. © 2024 Elsevier Inc.

关键词： Acoustic sensing Microphone array Posture detection Smart sensing

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Design and Implementation of Telemedicine System Using Light Fidelity and PIC16F877A Microcontroller

Design and Implementation of Telemedicine System Using Light...

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International Conference on information science and technology (ICIST)

作者： M.R. Ezilarasan Man-Fai Leung Xiangguang Dai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Instituteof Science and Technology Chennai India Faculty of Science and Engineering School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium in healthcare applications; however, the potential for electromagnetic interference with precision medical equipment remains a concern. This study introduces a novel design and implementation of a medical healthcare information system using the newly developed Wireless Visible Light Communication (VLC) technology, also known as Light Fidelity (Li-Fi). Visible Light Emitting Diodes (LEDs), expected to soon surpass traditional incandescent and fluorescent bulbs as the primary light source due to their energy efficiency, facilitate this technology. In this research, patient health is monitored using sensors, with results displayed on an LCD. An LED lightbulb transmits the data as light signals, which are captured by a photo-detector at the receiver's end. This setup not only displays the output on an LCD but also alerts caregivers through an auditory signal, beeping every 15 seconds for respiration and every 30 seconds for heart rate monitoring. Moreover, these lighting fixtures serve dual functions: illumination and wireless data transmission, using the rapid switching capability of LEDs. This prototype VLC-based system offers a promising alternative for data services and monitoring in hospital areas where RF use is restricted.

关键词： Wireless communication Radio frequency Wireless sensor networks Lighting Light emitting diodes Light fidelity Liquid crystal displays Communication system security Monitoring Visible light communication

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CONFIDENCE-GUIDED ADAPTIVE GATE AND DUAL DIFFERENTIAL ENHANCEMENT FOR VIDEO SALIENT OBJECT DETECTION

CONFIDENCE-GUIDED ADAPTIVE GATE AND DUAL DIFFERENTIAL ENHANC...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Chen, Peijia Lai, Jianhuang Wang, Guangcong Zhou, Huajun School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, spatial and temporal cues are often unreliable in real-world scenarios, such as low-contrast foreground, fast motion, and multiple moving objects. To address these problems, we propose a new framework to adaptively capture available information from spatial and temporal cues, which contains Confidence-guided Adaptive Gate (CAG) modules and Dual Differential Enhancement (DDE) modules. For both RGB features and optical flow features, CAG estimates confidence scores supervised by the IoU between predictions and the ground truths to re-calibrate the information with a gate mechanism. DDE captures the differential feature representation to enrich the spatial and temporal information and generate the fused features. Experimental results on four widely used datasets demonstrate the effectiveness of the proposed method against thirteen state-of-the-art methods. © 2021 IEEE

关键词： Object detection

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Deep learning approach for Parkinson’s screening with geometric features from spiral and wave drawings

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Multimedia Tools and Applications 2025年

作者： Malik, Meenakshi Onyema, Edeh Michael Uddin, Mueen Yadav, Poonam Sharma, Aanchal Jose, Jazlyn Shankar, Achyut Alasim, Fahad Abidi, Mustufa Haider BML Munjal University Gurugram India Department of Mathematics and Computer Science Coal City University Enugu Nigeria Adjunct Faculty Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India College of Computing & Information Technology University of Doha for Science and Technology Doha Qatar Starex University Gurugram India School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh201310 India Industrial Engineering Department College of Engineering King Saud University P. O. Box-800 Riyadh11421 Saudi Arabia Advanced Manufacturing Institute King Saud University Riyadh11421 Saudi Arabia

Parkinson’s disease (PD) is the second most common neurological ailment and affects a substantial percentage of the elderly population. A deterioration in handwriting is a common indication of Parkinson’s disease. Researchers have developed an algorithm-based tool to assist in distinguishing between Parkinson’s disease patients and healthy persons because the disease can be difficult to identify. Unfortunately, previous studies have not tackled this problem or adopted a preventive approach. Examining handwriting is one way to detect PD. After analyzing multiple publications, we examined the 2020 and 2022 articles from Convolutional Neural Network utilizing deep learning techniques. Spiral and wave photos of those in good health and those with Parkinson’s illness were employed in our collection. This investigation used the Random Forest, VGG-16, and ResNet-50 approaches. We applied ResNet-50 for the drawings and got 97% accuracy for spiral pictures over 10 epochs and 98% accuracy for wave images over eight epochs by using feature extraction and preprocessing techniques. This exceptional performance demonstrates ResNet-50’s potential as a potent tool for medical diagnostics, utilizing its deep learning skills to generate precise and reliable results compared to existing studies in which ResNet-50 has 83% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Quantum memory error correction computation based on Chamon model

arXiv

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arXiv 2023年

关键词： Error correction

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