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Advanced biomedical imaging for identifying blood cell type: Integrating segmentation, feature extraction, and GraphSAGE model

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Biomedical Engineering Advances

Biomedical engineering Advances 2025年 9卷

作者： Nur Mohammad Fahad Mohaimenul Azam Khan Raiaan Arefin Ittesafun Abian Ripon Kumar Debnath Sidratul Montaha Mirjam Jonkman Sami Azam Department of Computer Science and Engineering United International University Dhaka 1212 Bangladesh Faculty of Science and Technology Charles Darwin University Casuarina NT 0909 Australia Department of Computer Science University of Calgary Calgary AB T2N 1N4 Canada

Background The analysis of blood, including red blood cells (RBC) and different types of white blood cells (WBCs) plays a major role in the diagnosis of certain diseases. Automated segmentation of blood cells and their components can assist clinicians in effectively making diagnoses; however, it is quite challenging Objective: This study proposes a computerized approach to assessing the significance of biomedical imaging. It presents a framework for segmenting blood cells as well as their nuclei from the histopathological images of multiple datasets. Additionally, a custom algorithm is developed for blood cell counting. Methods This study introduces two automated methods for WBC analysis, including image segmentation to distinguish between WBCs and RBCs, the nuclei of the WBC, and classifying WBC types using clinically important features. An effective segmentation approach with image preprocessing algorithms is developed for automatic counting of WBCs and RBCs. An improved GraphSAGE model is constructed to classify blood cells. Clinically relevant features are extracted from segmented WBCs and nuclei for a final dataset. Feature ranking analysis identifies optimal features and reduces dimensionality, aiding graph dataset construction based on data similarity. Results Our proposed model achieved an accuracy of 96.67 %. A comparative analysis with benchmark models is done to assess the effectiveness of the model. The explainability of the model is addressed to enhance the transparency of the diagnostic system and provide insight into the decision-making process. Conclusion Leveraging the automated, simultaneous segmentation of blood cells and exploring their relationships for effective classification substantially helps to improve the reliability and applicability of this diagnostic system and aid clinicians.

关键词： Biomedical imaging Blood cell Segmentation Graph representation GraphSAGE model

Engineered Modular Neuronal Networks-on-Chip Represent Structure-Function Relationship

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SSRN

SSRN 2024年

作者： Habibey, Rouhollah Striebel, Johannes Latiftikhereshki, Roshanak Schmieder, Felix Nasiri, Rohollah Latifi, Shahrzad Universitäts-Augenklinik Bonn University of Bonn Dep. of Ophthalmology Ernst-Abbe-Straße 2 BonnD-53127 Germany CRTD - Center for Regenerative Therapies TU Dresden Dresden01307 Germany Dept. Neuroscience Italian Institute of Technology Genova Italy Department of Computer Engineering Faculty of Engineering Kermanshah Branch Azad University Kermanshah Iran Laboratory of Measurement and Sensor System Technique Faculty of Electrical and Computer Engineering TU Dresden Helmholtzstraße 18 Dresden01069 Germany Division of Nanobiotechnology Department of Protein Science Science for Life Laboratory KTH Royal Institute of Technology Solna Sweden AIMES Center for the Advancement of Integrated Medical and Engineering Sciences Department of Neuroscience Karolinska Institute Solna Sweden Department of Neurology David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Neuroscience Rockefeller Neuroscience Institute West Virginia University MorgantownWV26506 United States

Brain function is substantially linked to the highly organized structure of neuronal networks. The structures of in vitro assembled neuronal circuits often vary between samples and change over time, making it challenging to reliably record network functional output and link it to network structure. Therefore, engineering neuronal structures with predefined geometry and reproducible functional features is essential to robustly model in vivo neuronal circuits. Here, we engineered thin microchannel devices to assemble 2D and 3D modular networks. The microchannel devices were coupled with a multi-electrode array (MEA) electrophysiology system to enable long-term electrophysiology recordings from microengineered circuits. Each network consisted of 64 modules connected to their adjacent modules by micron-sized channels. The microstructures physically confined neurons to the recording electrodes, which significantly improved the efficiency of electrophysiology readout. In addition, the microstructures maintained the modular network structure for weeks. Modular circuits within microfluidic devices showed consistent spatial patterns of activity over weeks, which was lacking in the randomly formed circuits. The number of physical connections per module was shown to influence the measured activity and functional connectivity parameters, which represents the impact of network structure on its functional output. We show that microengineered 3D modular networks with profound activity and higher number of functional connections recapitulate key functional features of developing cortex. Structurally and functionally stable 2D and 3D networks mimic the modular architecture of brain circuits and provide a robust and reproducible in vitro microphysiological system for basic and translational neuroscience research. © 2024, The Authors. All rights reserved.

关键词： Microfluidics

Continuous-Variable Measurement-Device-Independent Quantum Key Distribution in Free-Space Channels

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arXiv 2022年

作者： Ghalaii, Masoud Pirandola, Stefano School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom Department of Computer Science University of York YorkYO10 5GH United Kingdom

The field of space communications is the realm of communication technologies where diffraction and atmospheric effects, both of which contribute to loss and noise, become overriding. The pertinent questions here are how and at which rate information (secret keys) can be securely transferred (shared) among users under such supposedly severe circumstances. In the present work we study continuous-variable (CV) quantum key distribution (QKD) in a measurement-device-independent (MDI) configuration over free-space optical (FSO) links. We assess the turbulence regime and provide a composable finite-size key rate analysis of the protocol for FSO links. We study both short-range, horizontal communication links as well as slant paths to, e.g., high-altitude platform station (HAPS) systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum cryptography

Intelligent Reflective Surface Assisted Integrated Sensing and Wireless Power Transfer

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arXiv 2023年

作者： Li, Zheng Zhu, Zhengyu Chu, Zheng Guan, Yingying Mi, De Liu, Fan Yang, Lie-Liang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China National Mobile Communications Research Laboratory Southeast University Nanjing210019 China University of Surrey GuildfordGU2 7XH United Kingdom School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. In light of the performance tradeoff between energy harvesting and sensing, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first one involves a semi-definite program technique, while the second one features a low-complexity optimization algorithm based on successive convex approximation and majorization minimization. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy transfer

Fiber-integrated microwave-to-optical quantum transducer

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Physical Review Applied 2023年第4期20卷 044031-044031页

作者： Wenfang Li Jinjin Du C. M. Wilson Michal Bajcsy Institute for Quantum Computing University of Waterloo 200 University Avenue West Waterloo Ontario N2L 3G1 Canada Okinawa Institute of Science and Technology Graduate University Onna Okinawa 904-0495 Japan Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West Waterloo Ontario N2L 3G1 Canada

We propose an on-chip fiber-based quantum transducer to convert microwave photons into optical-telecommunication-band photons. The device consists of a coplanar-waveguide microwave resonator and a pair of coupled optical cavities integrated into a rare-earth-doped fiber. The microwave resonator is formed by two aluminum strips deposited onto the cladding of the fiber, whose diameter has been etched down to approximately 8μm. We study the conversion process using the cavity electro-optic formalism for a system operating at liquid-helium temperature (around 4 K), as well as using a three-wave mixing approach for operation at millikelvin temperatures achievable in a dilution refrigerator. Both methods predict that unity conversion efficiency could be achievable with realistically achievable experimental parameters. The proposed device acts as a quantum transducer for coherent microwave-to-optical conversion, which has great promise for application in all-fiber integrated quantum devices for quantum information processing.

关键词： Integrated optics Light-matter interaction Optical quantum information processing Quantum communication Optical fibers Transducers Microwave techniques Optical techniques

Quantum-Secured Space-Air-Ground Integrated Networks: Concept, Framework, and Case Study

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arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Xiong, Zehui Kang, Jiawen Cao, Xianbin Shen, Xuemin Sherman Miao, Chunyan The School of Computer Science and Engineering Nanyang Technological University Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Automation Guangdong University of Technology China The School of Electronic and Information Engineering Beihang University Beijing100191 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

In the upcoming 6G era, existing terrestrial networks have evolved toward space-air-ground integrated networks (SAGIN), providing ultra-high data rates, seamless network coverage, and ubiquitous intelligence for communications of applications and services. However, conventional communications in SAGIN still face data confidentiality issues. Fortunately, the concept of Quantum Key Distribution (QKD) over SAGIN is able to provide information-theoretic security for secure communications in SAGIN with quantum cryptography. Therefore, in this paper, we propose the quantum-secured SAGIN which is feasible to achieve proven secure communications using quantum mechanics to protect data channels between space, air, and ground nodes. Moreover, we propose a universal QKD service provisioning framework to minimize the cost of QKD services under the uncertainty and dynamics of communications in quantum-secured SAGIN. In this framework, fiber-based QKD services are deployed in passive optical networks with the advantages of low loss and high stability. Moreover, the widely covered and flexible satellite- and UAV-based QKD services are provisioned as a supplement during the real-time data transmission phase. Finally, to examine the effectiveness of the proposed concept and framework, a case study of quantum-secured SAGIN in the Metaverse is conducted where uncertain and dynamic factors of the secure communications in Metaverse applications are effectively resolved in the proposed framework. © 2022, CC BY.

关键词： Quantum cryptography

Explainable Deep Learning Framework for SERS Bio-quantification

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arXiv 2024年

作者： Zaki, Jihan K. Tomasik, Jakub McCune, Jade A. Bahn, Sabine Liò, Pietro Scherman, Oren A. Melville Laboraratory for Polymer Synthesis Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Rd CB2 1EW United Kingdom Department of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive CB3 0AS United Kingdom Department of Computer Science and Technology University of Cambridge 15 JJ Thomson Ave CB3 0FD United Kingdom

Surface-enhanced Raman spectroscopy (SERS) is rapidly gaining attention as a potential fast and inexpensive method for biomarker quantification, which can be combined with deep learning methodology to elucidate complex biomarker-disease relationships. Current standard practices in computational SERS analysis are substantially behind the state-of-the-art machine learning approaches;however, present challenges of SERS analysis could be effectiely addressed with a robust computational framework. Additionally, there is a particular need for improved model explainability for SERS analysis. While current methods are capable of providing global explainability, they are insufficient in assessing the lower level contexts where other factors, such as confounding factors, outliers and measurement errors, or different predictors of the same outcome variable, could affect prediction outcomes. This study presents a novel framework for SERS bio-quantification rooted in a three-step process, including spectral processing, analyte quantification, and model explainability. To this end, a serotonin quantification task in a urine medium was assessed as a model task with 682 SERS spectra measured in a micromolar range using gold nanoparticles and cucurbit[8]uril chemical spacers. A denoising autoencoder was developed for spectral enhancement, convolutional neural networks (CNN), and vision transformers were utilized for biomarker quantification. Lastly, a novel context representative interpretable model explanations (CRIME) method was developed to suit the current needs of SERS mixture analysis explainability. Serotonin quantification was most efficient in denoised spectra analysed using a convolutional neural network with a three-parameter logistic output layer (Validation: mean absolute error (MAE) = 0.24 µM, mean percentage error (MPE) = 15.00%, Test: MAE = 0.15 µM, MPE = 4.67%). Subsequently, the CRIME method revealed the CNN model to present six unique prediction contexts, of which

关键词： Convolutional neural networks

Building Automation System Data Integration with BIM: Data Structure and Supporting Case Study

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arXiv 2022年

作者： Quinn, Caroline Shabestari, Ali Zargar Gilani, Sara Misic, Tony Litoiu, Marin McArthur, Jenn J. Department of Architectural Science Ryerson University 350 Victoria St. TorontoONM5B 2K3 Canada Department of Computer Science Ryerson University 350 Victoria St. TorontoONM5B 2K3 Canada Department of Electrical & Computer Engineering York University LAS 1012M 4700 Keele St. TorontoONM3J 1P3 Canada

Buildings Automation Systems (BAS) are ubiquitous in contemporary buildings, both monitoring building conditions and managing the building system control points. At present, these controls are prescriptive and pre-determined by the design team, rather than responsive to actual building performance. These are further limited by prescribed logic, possess only rudimentary visualizations, and lack broader system integration capabilities. Advances in machine learning, edge analytics, data management systems, and Facility Management-enabled Building Information Models (FM-BIMs) permit a novel approach: cloud-hosted building management. This paper presents an integration technique for mapping the data from a building Internet of Things (IoT) sensor network to an FM-BIM. The sensor data naming convention and time-series analysis strategies integrated into the data structure are discussed and presented, including the use of a 3D nested list to permit time-series data to be mapped to the FM-BIM and readily visualized. The developed approach is presented through a case study describing the scalability of the approach to integrate a building BAS system with a BIM. The resultant data structure and key visualizations are presented to demonstrate the value of this approach, which permits the end-user to select the desired timeframe for visualization and readily step through the spatio-temporal building performance *** Codes 68M10 (Primary), 93B12 (Secondary) © 2022, CC BY-NC-ND.

关键词： Data visualization

HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

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arXiv 2023年

作者： Islam, Md Shofiqul Hasan, Khondokar Fida Sultana, Sunjida Uddin, Shahadat Lio', Pietro Quinn, Julian M.W. Moni, Mohammad Ali Faculty of Computing Universiti Malaysia Pahang Gambang Pahang Kuantan26300 Malaysia Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia 2 George Street Brisbane4000 Australia Department of Computer Science and Engineering Islamic University Kushtia7600 Bangladesh School of Project Management Faculty of Engineering The University of Sydney Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Bone Research Group The Garvan Institute of Medical Research DarlinghurstNSW Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach substantially reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise. © 2023, CC BY.

关键词： Electrocardiograms