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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Kalantzopoulos, Charalampos Pezoulas, Vasileios C. Koutsouris, Dimitrios D. Matsopoulos, George K. Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. National Technical University of Athens School of Electrical and Computer Engineering Biomedical Engineering Laboratory AthensGR15773 Greece Forth Biomedical Research Institute Ioannina Greece University of Ioannina Ioannina Greece Forth Institute of Computer Science Heraklion Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece FORTH-IMBB Ioannina Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece

ISBN: (纸本)9798350383386

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland's complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model's performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net's deficiency leading to the development of more specialized models in respect to these factors. © 2023 IEEE.

关键词： Urology

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MRI of a Surgical Guidewire in a Hydrogel Phantom: Effects of Ferric Ion Chelated Natural MNPs

MRI of a Surgical Guidewire in a Hydrogel Phantom: Effects o...

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2023 Medical Technologies Congress, TIPTEKNO 2023

作者： Baysoy, Engin Ayan, Bugra Kaleli-Can, Gizem Buyuksarac, Bora Liu, Chunlei Bahçeşehir University Department of Biomedical Engineering İstanbul34353 Turkey Stanford University Department of Cardiothoracic Surgery StanfordCA94305 United States İzmir Democracy University Department of Biomedical Engineering İzmir35140 Turkey University of California Berkeley Department of Electrical Engineering and Computer Sciences BerkeleyCA94720 United States

ISBN: (纸本)9798350328967

Magnetic resonance imaging (MRI) presents an alternative imaging method over conventional X-ray based modalities for guiding minimal invasive surgeries. However, lack of commercial MRI safe and functional interventional instruments and devices is the main obstacle for the realization of interventional procedures under MRI. Herein, as a novel passive tracking method, Fe3+ ions binded melanin nanoparticles (MNPs) were used as a coating to enhance the visibility of a commercial MRI safe nitinol guidewire. Fe3+ chelating ability of MNPs was found to be approximately 95% using inductively coupled plasma mass spectroscopy (ICP-MS). The spherical shapes of MNPs were evaluated and measured by scanning electron analysis with a mean diameter of 134± 21 nm for MNPs before $Fe3+ chelation and $144 ± 34$ nm after. NIH 3T3 cells incubated with MNPs-Fe3+ solutions at levels up to 1000 ng/ml and MNPs-Fe3+ solutions indicated negligible cytotoxic effect. The traceability of MNPs-Fe+ coated surgical nitinol guidewire was confirmed inside a custom-made hydrogel phantom model in a 3T MRI scanner. A conspicuous bright signal from coated nitinol guidewire was obtained for axial, sagittal, and coronal planes with MRI. This pilot study showed that both the tracking of an interventional guidewire and the degradation level of a hydrogel used for the therapeutic response of a drug can be imaged by MRI during minimal invasive operations. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Optimal Integration of Hybrid FES-Exoskeleton for Precise Knee Trajectory Control

Optimal Integration of Hybrid FES-Exoskeleton for Precise Kn...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Masoud Jafaripour Vivian Mushahwar Mahdi Tavakoli Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering and Department of Biomedical Engineering University of Alberta Department of Medicine Division of Physical Medicine and Rehabilitation University of Alberta

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a novel hybrid torque allocation method for improving wearability and mobility in integrated functional electrical stimulation (FES) of the quadriceps muscles and powered exoskeleton systems. Our proposed approach leverages a hierarchical closed-loop controller for knee joint position tracking while addressing limitations of powered exoskeletons and FES systems by reducing power consumption and battery size and by mitigating FES-induced muscle fatigue, respectively. The core component is a model-free optimization algorithm that dynamically distributes torque between FES and the exoskeleton by considering tracking error, effort, and the prediction of muscle fatigue in the cost function, computing allocation gain in an online manner. The online optimization approach interactively changes the optimal allocation gain by taking into account the instantaneous value of error and effort and also penalizing FES-induced fatigue, a common challenge in long-duration experiments. The results demonstrate that this dynamic allocation significantly improves system wearability by reducing power consumption without increasing muscle fatigue during the extension phase of walking. This hybrid control approach contributes to improving exoskeleton wearability and rehabilitation outcomes for individuals with SCI and mobility impairments, enhancing assistive technology and quality of life.

关键词： Knee Torque Power demand Exoskeletons Wearable robots Muscles Fatigue Iron Trajectory Resource management

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Modified Imperialist Competitive Algorithm (MICA) For Smart Heart Disease Prediction in IoT System 2

Modified Imperialist Competitive Algorithm (MICA) For Smart ...

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2nd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Thangarasan Dhana Sony, J. Premkumar, M. Patro, Pramoda Samson Isaac, J. Maniraj, P. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India VelTech Rangarajan Dr.Sagunthala R&d Institute of Science Department Of. Biomedical Engineering Chennai India Mahendra Institute of Technology Department of Information Technology Namakkal India College of Engineering Department of Engineering Mathematics Hyderabad India Karunya Institute of Technology and Sciences Department of Biomedical Engineering Coimbatore India M. Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350347456

For the detection and prognosis of heart disease, Internet of Medical Things (IoMT) technology has recently been implemented in healthcare systems. The intended study's main objective is to foresee heart illness using medical data and imaging to classify data. Preprocessing is done on the input dataset to deal with missing values and incorrect data. IoT devices analyse the data they receive from patients, physicians, or nurses using the Modified Imperialist Competitive Algorithm (MICA). The IoT device's analysis of the data allows for effective and informed judgements to be made by humans, robots, and even other IoT devices. A modified imperialist competitive algorithm is suggested in this research in order to pinpoint the essential characteristics of heart disease. The Modified Imperialist Competitive Algorithm is used to select features for the diagnosis of heart disease (MICA). The improved self-adaptive Bayesian algorithm (ISABA) technique is then used to classify the chosen features into normal and abnormal states. For detecting normal sensor data and abnormal sensor data, respectively, the ISABA approach achieved accuracy of 96.85% and 98.31%. With a 96.32% specificity and a 99.15% maximum accuracy in categorizing images, the proposed model outperformed the competition © 2023 IEEE.

关键词： Internet of things

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An implementation, evaluation and validation of a dynamic fire and conflagration risk indicator for wooden homes 15

An implementation, evaluation and validation of a dynamic fi...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Strand, R.D. Kristensen, L.M. Department of Safety Chemistry and Biomedical Laboratory Sciences Western Norway University of Applied Sciences Haugesund Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The pervasive presence of IoT sensing devices combined with advances in cloud-based services has made historical- and forecast weather data services widely available for use in smart software systems. The contribution of this paper is the design, performance evaluation, and validation of a software implementation of model-based dynamic fire risk prediction for wooden homes using local weather data. A key feature of the implementation is that the software architecture has been designed to support the use of different underlying cloud-based weather data services, and the integration as a service in third-party smart systems embedding fire risk predictions. The performed evaluation shows that the implementation is efficient, as weather data can be retrieved, preprocessed and fire risk predictions computed within seconds using only in the order of Kb's of memory;and accurate considering our sets of data, as the fire risks computed by the implementation have been validated against a set of in-situ measurements. © 2024 Elsevier B.V.. All rights reserved.

关键词： Fires

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Internet of Things (IoT) Feedback System using Raspberry Pi 7

Internet of Things (IoT) Feedback System using Raspberry Pi

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Pradeepkumar, G. Ramraj, B. Ayyannan, Manimuthu Rohith Bhat, C. Kalavathi Devi, T. Kumar, Neelam Sanjeev Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electrical and Electronics Engineering Nandha Engineering College Erode India San Francisco Bay Area CA United States Institute of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electronics and Instrumentation Engineering Kongu Engineering College Tamil nadu Erode India Department of Biomedical Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9781665464086

Constant monitoring and examination of customer input in modern businesses, public spaces, and institutional settings necessitates a significant investment in record-keeping equipment. This study includes a feedback mechanism to help with system documentation and maintenance. In the context of this work, "feedback" refers to the response of the surrounding environment to a specific activity. This study tracks the client reviews by physically rating them and then uploading the results to the cloud. Anyone with access can see how most of the comments were received. With this system in place, monitoring the status of each item of inputis a breeze. © 2023 IEEE.

关键词： Internet of things

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Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization 38

Unsupervised Homography Estimation on Multimodal Image Pair ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Song, Sanghyeob Lew, Jaihyun Jang, Hyemi Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Samsung Electro-Mechanics Korea Republic of

Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-truth data. In response, unsupervised learning approaches have emerged. Most early methods, though, assume that the given image pairs are from the same camera or have minor lighting differences. Consequently, while these methods perform effectively under such conditions, they generally fail when input image pairs come from different domains, referred to as multimodal image pairs. To address these limitations, we propose AltO, an unsupervised learning framework for estimating homography in multimodal image pairs. Our method employs a two-phase alternating optimization framework, similar to Expectation-Maximization (EM), where one phase reduces the geometry gap and the other addresses the modality gap. To handle these gaps, we use Barlow Twins loss for the modality gap and propose an extended version, Geometry Barlow Twins, for the geometry gap. As a result, we demonstrate that our method, AltO, can be trained on multimodal datasets without any ground-truth data. It not only outperforms other unsupervised methods but is also compatible with various architectures of homography estimators. The source code can be found at: https://***/songsang7/AltO. © 2024 Neural information processing systems foundation. All rights reserved.

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Improved Bat Algorithm with Deep Learning-Based biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Role of Consumption Pattern in Optimal Allocation of Distributed Generators in Electric Power and Energy Systems

Role of Consumption Pattern in Optimal Allocation of Distrib...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Mahdavi, Meisam Jurado, Francisco Schmitt, Konrad Chamana, Manohar Bayne, Stephen University of Jaén Linares Department of Electrical Engineering Jaén Spain Texas Tech University Electrical & Computer Engineering Department LubbockTX United States Texas Tech University Renewable Energy Program LubbockTX United States

ISBN: (纸本)9781665490719

The placement of distributed generation (DG) units in power systems is an efficient way for energy loss reduction, especially when the penetration of DG in modern systems is growing due to their impacts on environmental sustainability. On the other hand, load variations and methods of electricity consumption affect energy losses amount. Therefore, power demand variations have an essential role in the determination of energy loss amount and optimal generation of DG. However, considering the variability of load level in the DG allocation problem increases the burden and computational time, and neglecting it causes the energy losses to be calculated inaccurately. Therefore, this paper aims to evaluate the effect of load patterns on renewable DG allocation plans in order to find out the importance of considering load variations in energy loss minimization via DG placement. The analysis has been conducted on 7-, 12-, 16-, 28-, 30-, 33-, 59-, 69-, 70-, 84-, and 119-bus distribution systems by a classic optimization tool named AMPL. © 2023 IEEE.

关键词： Distributed power generation

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Analysis of feedback schemes in proliferating stem cells 63

Analysis of feedback schemes in proliferating stem cells

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Borri, A. Palumbo, P. Singh, A. Via dei Taurini 19 Roma Italy University of Milano-Bicocca Department of Biotechnologies and Biosciences Milan20126 Italy University of Delaware Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology NewarkDE19716 United States

ISBN: (纸本)9798350316339

Stem cells have emerged as a pivotal player in biomedical research, offering unprecedented opportunities for regenerative medicine, disease modeling, and drug discovery. These undifferentiated cells possess the unique ability to self-renew and differentiate into various specialized cell types, making them invaluable in tissue repair and regeneration. This note analyzes a stochastic model aiming at unraveling the control machinery providing the proper ratio of stem-versus-differentiated cells, in agreement with a couple of feedback schemes regulating cell proliferation. What emerges from the closed-loop schemes analysis is that in order to have an asymptotically stable, nontrivial accumulation of differentiated cells a negative feedback on cell proliferation is required. Numerical simulations in the stochastic setting validate the approximate theoretical results achieved to relate how random fluctuations propagate with respect to variations of model parameters. © 2024 IEEE.

关键词： Stochastic systems

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