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IEEE MTT-S International Microwave Bio Conference ((IMBIOC)

作者： Mao-Hsiang Huang Sen Bing Mohamed Benomar Hung Cao J.-C. Chiao Department of Electrical Engineering and Computer Science University of California Irvine Irvine CA USA Department of Electrical and Computer Engineering Southern Methodist University Dallas TX USA Department of Electrical Engineering and Computer Science Biomedical Engineering Computer Science University of California Irvine Irvine CA USA

ISBN: (数字)9798331510473

ISBN: (纸本)9798331510480

This work introduces a refined image-processing method explicitly designed to amalgamate raster-scanned images obtained with a compact planar microwave resonator. The resonator tuned for use on the skin provides a deeper field penetration into the dermis layer, compared to the skin impedance measurements. The resonance of the sensor is sensitive to the dielectric properties of the tissues underneath the skin. Healthy and abnormal tissues exhibit different dielectric characteristics. Pixelated images can leverage resonant frequencies and reflection coefficients to enhance contrasts and boundaries to identify the abnormal tissues. We proposed using analytical tools like the structural similarity index to correlate frequency shifts with reflection coefficient magnitudes precisely. Preliminary results suggested the adeptness of the technique at classifying whether the tissue depth surpasses 15 mm and detecting tumor locations with a high accuracy when the image depth does not exceed 15 mm. A compact neural network model was implemented for processing the microwave images when the tumor depth exceeded 15 mm and achieved a high Dice coefficient. The work incorporated simulations to substantiate the novel concept and demonstrated its potential for noninvasive, efficient, and cost-effective subcutaneous imaging in medical applications.

关键词： Accuracy Neural networks Imaging Resonant frequency Microwave theory and techniques Dielectrics Reflection coefficient Microwave imaging Tumors Testing

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Progressive Deep Learning Approach for Breast Cancer Diagnosis in Thermal Images

Progressive Deep Learning Approach for Breast Cancer Diagnos...

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IEEE International Conference on biomedical engineering, computer and Information Technology for Health

作者： Nasir Ayuba Danmusa Osim Kumar Pal Habeeb Dayo Yusuff Department of Electrical & Computer Engineering Biomedical Engineering University of Patras Patras Greece Biomedical Engineering University of Strasbourg Strasbourg Grand Est France

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

Worldwide, breast cancer is one of the leading causes of cancer-related deaths. However, death rates can be considerably lowered by detecting the disease early with imaging techniques like mammography and ultrasound. Medical thermal imaging is a useful non-invasive technique to spot anomalies. Early diagnosis and improved screening accuracy are being achieved through the application of machine learning-based risk prediction models. For the purpose of feature extraction and breast cancer classification, this work makes use of five cutting-edge convolutional neural network models as well as vision transformers. Annotated thermal images of both healthy and diseased breast tissues are included in the dataset. Features are extracted and concatenated for Independent Component Analysis (ICA). A Random Forest classifier is then trained using the updated features. This method highlights the advantages of integrating feature transformation techniques with CNN architectures to increase classification accuracy.

关键词： Deep learning computer vision Accuracy computer architecture Feature extraction Transformers Breast cancer Convolutional neural networks Random forests Medical diagnostic imaging

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Langevin Equation in Heterogeneous Landscapes: How to Choose the Interpretation

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Physical Review Letters 2024年第6期133卷 067102-067102页

作者： Adrian Pacheco-Pozo Michał Balcerek Agnieszka Wyłomanska Krzysztof Burnecki Igor M. Sokolov Diego Krapf Department of Electrical and Computer Engineering and School of Biomedical Engineering Faculty of Pure and Applied Mathematics Hugo Steinhaus Center Institut für Physik and IRIS Adlershof

The Langevin equation is a common tool to model diffusion at a single-particle level. In nonhomogeneous environments, such as aqueous two-phase systems or biological condensates with different diffusion coefficients in different phases, the solution to a Langevin equation is not unique unless the interpretation of stochastic integrals involved is selected. We analyze the diffusion of particles in such systems and evaluate the mean, the mean square displacement, and the distribution of particles, as well as the variance of the time-averaged mean-square displacements. Our analytical results provide a method to choose the interpretation parameter from single-particle tracking experiments.

关键词： Brownian motion Diffusion Random walks Stochastic processes Liquid-liquid interfaces Langevin equation Stochastic differential equations

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Measurements of slow tissue dynamics with short-separation speckle contrast optical spectroscopy

Measurements of slow tissue dynamics with short-separation s...

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Optics and the Brain, BRAIN 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Liu, Bingxue Boas, David A. Cheng, Xiaojun Department of Electrical and Computer Engineering Boston University MA02215 United States Neurophotonics Center Boston University MA02215 United States Department of Biomedical Engineering Boston University MA02215 United States

We present a short-separation speckle contrast optical spectroscopy (ss-SCOS) system to enhance the static and slow tissue components with the multi-exposure time-based speckle contrast model to extract both the slow ... 详细信息

ISBN: (纸本)9781957171210

关键词： Blood Laser imaging Laser speckle Optical spectroscopy Speckle patterns Tissue

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Optimizing Gabor texture features for materials recognition by convolutional neural networks

Optimizing Gabor texture features for materials recognition ...

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London Imaging Meeting, LIM 2023

作者： Bianconi, Francesco Cusano, Claudio Napoletano, Paolo Schettini, Raimondo Department of Engineering Universitá degli Studi di Perugia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Department of Informatics Systems and Communication University of Milano Bicocca Italy

ISBN: (纸本)9780892083633

In this paper, we present a novel technique that allows for customized Gabor texture features by leveraging deep learning neural networks. Our method involves using a Convolutional Neural Network to refactor traditional, hand-designed filters on specific datasets. The refactored filters can be used in an off-the shelf manner with the same computational cost but significantly improved accuracy for material recognition. We demonstrate the effectiveness of our approach by reporting a gain in discrimina tion accuracy on different material datasets. Our technique is particularly appealing in situations where the use of the entire CNN would be inadequate, such as analyzing non-square im ages or performing segmentation tasks. Overall, our approach provides a powerful tool for improving the accuracy of material recognition tasks while retaining the advantages of handcrafted filters. © 2023 Society for Imaging Science and Technology.

关键词： Convolutional neural networks

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Analysis of Video Lessons: a Case for Smart Indexing and Topic Extraction 1

Analysis of Video Lessons: a Case for Smart Indexing and Top...

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1st Italian Conference on Big Data and Data Science, ITADATA 2022

作者： Arazzi, Marco Ferretti, Marco Nocera, Antonino Department of Electrical Computer and Biomedical Engineering University of Pavia Via Ferrata 5 Pavia27100 Italy

On-line teaching activity during the pandemic has generated huge amounts of data, mainly in the form of video. Leveraging this rich source of didactic material requires at least two somewhat complementary facilities: smart indexing into the corpus of lessons, and automatic extraction of a list of topics that best represent the main subject of a course, based on the set of its lessons. This paper shows a preliminary attempt to address both issues, using material produced at the University of Pavia in the year 2020, specifically a prototype bachelor course to develop the tools and the methods to be later applied on the big data repository of the whole set of lessons available. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Indexing (of information)

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Facilitating Learning Analytics in Histology Courses with Knowledge Graphs 20

Facilitating Learning Analytics in Histology Courses with Kn...

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Joint of Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems, SEMANTiCS-PDWT 2024

作者： Walraff, Jimmy Coco, Andreas Delporte, Guillaume Michel, Merlin Fries, Allyson Defaweux, Valérie Debruyne, Christophe Montefiore Institute of Electrical Engineering and Computer Science University of Liège Liège Belgium Department of Biomedical and Preclinical Sciences Faculty of Medicine University of Liège Liège Belgium

We report on an ongoing learning analytics project at the University of Liège, in which we want to analyze student interactions on Cytomine for a histology *** provides tools for medical image annotation and an API that has been used for learning *** problem, however, is that the data obtained from Cytomine has implicit semantics and requires many data preprocessing and integration *** poster presents the prototype KG we have built to address these *** KG adopts PROV-O to distinguish activities from their outcomes, addressing some of the issues faced in the *** also demonstrate that the KG can be used in Jupyter notebooks, though learning analytics is left for future *** did demonstrate that the data analysis process has become more declarative and transparent, as data is analyzed starting from SPARQL *** focused on one project in Cytomine, and future work consists of integrating additional *** also plan to investigate the development of more self-contained KG generation techniques as we have no direct access to the Cytomine application. © 2024 Copyright for this paper by its authors.

关键词： Medical imaging

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Automatic LPI Radar Waveform Recognition Using Vision Transformer

Automatic LPI Radar Waveform Recognition Using Vision Transf...

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2023 IEEE International Radar Conference, RADAR 2023

作者： Kim, Junseob Cho, Sunghwan Hwang, Sunil Choi, Yeongyoon Korea Military Academy Department of Electrical Engineering 01805 Korea Republic of Defense Acquisition Program Administration Maneuver System Coordination and Contract Team 13809 Korea Republic of Gist Department of Electrical Engineering and Computer Science 61005 Korea Republic of

ISBN: (纸本)9781665482783

This paper investigates the classification of low probability of intercept (LPI) radar signals by exploiting the intrinsic advantages of the Vision Transformer (ViT). Due to the characteristics of LPI radar signals, such as intrapulse modulation, wide frequency bands, and low transmission power, these signals are challenging to be detected and classified using traditional analytic methods. This has led to the adoption of various deep learning techniques to overcome these limitations. On the one hand, the ViT, originally developed for natural language processing, has demonstrated outstanding performance in computer vision by replacing the structure of the convolutional neural network (CNN) with the transformer, specifically leveraging self-attention. Therefore, this paper explores a method based on the ViT technique for classifying LPI signal images. The simulation results show that the proposed ViT method outperforms the traditional CNN method by 12.8% at -10dB SNR. © 2023 IEEE.

关键词： Convolutional neural networks

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A Study on Intelligent Optical Bone Densitometry

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IEEE Journal of Translational engineering in Health and Medicine 2024年 12卷 401-412页

作者： Meitei, Takhellambam Gautam Chang, Wei-Chun Cheong, Pou-Leng Wang, Yi-Min Sun, Chia-Wei Taipei Medical University Wan Fang Hospital Department of Orthopedic Surgery Taipei110 Taiwan National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Photonics Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Department of Biological Science and Technology Hsinchu30010 Taiwan National Taiwan University Hospital Hsinchu Branch Department of Pediatrics Hsinchu300 Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Medical Device Innovation and Translation Center Taipei City112 Taiwan

Osteoporosis is a prevalent chronic disease worldwide, particularly affecting the aging population. The gold standard diagnostic tool for osteoporosis is Dual-energy X-ray Absorptiometry (DXA). However, the expensive cost of the DXA machine and the need for skilled professionals to operate it restrict its accessibility to the general public. This paper builds upon previous research and proposes a novel approach for rapidly screening bone density. The method involves utilizing near-infrared light to capture local body information within the human body. Deep learning techniques are employed to analyze the obtained data and extract meaningful insights related to bone density. Our initial prediction, utilizing multi-linear regression, demonstrated a strong correlation (r = 0.98, p-value = 0.003∗∗) with the measured Bone Mineral Density (BMD) obtained from Dual-energy X-ray Absorptiometry (DXA). This indicates a highly significant relationship between the predicted values and the actual BMD measurements. A deep learning-based algorithm is applied to analyze the underlying information further to predict bone density at the wrist, hip, and spine. The prediction of bone densities in the hip and spine holds significant importance due to their status as gold-standard sites for assessing an individual's bone density. Our prediction rate had an error margin below 10% for the wrist and below 20% for the hip and spine bone density. © 2013 IEEE.

关键词： Bone

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The Effect of Lead Winding Near IPG on AIMD Models under MR RF Exposure

The Effect of Lead Winding Near IPG on AIMD Models under MR ...

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2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Zuo, Ziyu Wang, Qingyan Zheng, Jianfeng Jeong, Hongbae Kumar, Ananda Chen, Ji University of Houston Department of Electrical and Computer Engineering HoustonTX United States Us Food and Drug Administration Division of Biomedical Physics Osel Cdrh Silver SpringMD United States

ISBN: (纸本)9798350360394

This paper explores the effects of lead winding near an Implantable Pulse Generator (IPG) on a Transfer Function (TF) model under Magnetic Resonance Imaging (MRI) Radio Frequency (RF) exposure. Three commercial AIMD systems are used in the study. Multiple sets of measurement are carefully conducted. Revealing that the TF model remains unchanged for one AIMD, while it undergoes significant changes for the other two systems. Upon closer examination of the leads' structure, it is observed that leads with thicker insulation layer tend to preserve the TF model. This observation is in agreement with the transmission line theory, wherein the AIMD with a thick insulation layer can preserve a low lossy transmission line mode in contrast to the other two devices. © 2024 IEEE.

关键词： Electric windings

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