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arXiv 2025年

作者： Rego, Brenner S. Scott, Joseph K. Raimondo, Davide M. Terra, Marco H. Raffo, Guilherme V. Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Engineering and Architecture University of Trieste Trieste34127 Italy Department of Electronics Engineering Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely zonotopes, constrained zonotopes, and line zonotopes, in addition to a basic implementation of interval arithmetic. This library has capabilities starting from the basic set operations with these sets, including propagations through nonlinear functions using various approximation methods. The features of ZETA allow for reachability analysis and state estimation of discrete-time linear, nonlinear, and descriptor systems, in addition to active fault diagnosis of linear systems. Efficient order reduction methods are also implemented for the respective set representations. Some examples are presented in order to illustrate the functionalities of the new library. © 2025, CC BY.

关键词： Digital arithmetic

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Motion artifact reduction in quantitative susceptibility mapping using deep neural network

arXiv

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arXiv 2021年

作者： Li, Chao Zhang, Hang Zhang, Jinwei Spincemaille, Pascal Nguyen, Thanh D. Wang, Yi Department of Applied and Engineering Physics Cornell University IthacaNY United States Department of Radiology Weill Medical College of Cornell University New YorkNY United States Department of Electrical and Computer Engineering Cornell University IthacaNY United States Department of Biomedical Engineering Cornell University IthacaNY United States

An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles to simulate motion-corrupted QSM images. The simulated QSM image is paired with its motion-free reference to train a neural network using supervised learning. The trained network is tested on unseen simulated motion-corrupted QSM images, in healthy volunteers and in Parkinson's disease patients. The results show that motion artifacts, such as ringing and ghosting, were successfully suppressed. © 2021, CC BY.

关键词： Deep neural networks

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AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET/CT for detection of cardiac sarcoidosis

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Journal of Nuclear Cardiology 2025年 48卷 102195页

作者： Miller, Robert JH. Shanbhag, Aakash Marcinkiewicz, Anna M. Struble, Helen Gransar, Heidi Hijazi, Waseem Fujito, Hidesato Kransdorf, Evan Kavanagh, Paul Liang, Joanna X. Builoff, Valerie Dey, Damini Berman, Daniel S. Slomka, Piotr J. Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Center of Radiological Diagnostics National Medical Institute of the Ministry of the Interior and Administration Warsaw Poland

Background: [18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) plays a central role in diagnosing and managing cardiac sarcoidosis. We propose a fully automated pipeline for quantification of [18F]FDG PET activity using deep learning (DL) segmentation of cardiac chambers on computed tomography (CT) attenuation maps and evaluate quantitative approaches based on this framework. Methods: We included consecutive patients undergoing [18F]FDG PET/CT for suspected cardiac sarcoidosis. DL segmented left atrium, left ventricle (LV), right atrium, right ventricle, aorta, LV myocardium, and lungs from CT attenuation scans. CT-defined anatomical regions were applied to [18F]FDG PET images automatically to quantify target to background ratio (TBR), volume of inflammation (VOI) and cardiometabolic activity (CMA) using full sized and shrunk segmentations. Results: A total of 69 patients were included, with mean age of 56.1 ± 13.4 and cardiac sarcoidosis present in 29 (42 %). CMA had highest prediction performance (area under the receiver operating characteristic curve [AUC] .919, 95 % confidence interval [CI] .858 – .980) followed by VOI (AUC .903, 95 % CI .834 – .971), TBR (AUC .891, 95 % CI .819 – .964), and maximum standardized uptake value (AUC .812, 95 % CI .701 – .923). Abnormal CMA (≥1) had a sensitivity of 100 % and specificity 65 % for cardiac sarcoidosis. Lung quantification was able to identify patients with pulmonary abnormalities. Conclusion: We demonstrate that fully automated volumetric quantification of [18F]FDG PET for cardiac sarcoidosis based on CT attenuation map-derived volumetry is feasible, rapid, and has high prediction performance. This approach provides objective measurements of cardiac inflammation with consistent definition of myocardium and background region. © 2025 American Society of Nuclear Cardiology

关键词： Cardiac sarcoidosis Deep learning Hypermetabolism Positron emission tomography Quantification

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Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

arXiv

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arXiv 2023年

作者： Azad, Reza Jia, Yiwei Aghdam, Ehsan Khodapanah Cohen-Adad, Julien Merhof, Dorit The Institute of Imaging and Computer Vision RWTH Aachen University Aachen52074 Germany The Department of Electrical Engineering Shahid Beheshti University Tehran*** Iran The Mila Quebec AI Institute Canada The Functional Neuroimaging Unit CRIUGM University of Montreal Canada The NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal MontrealH3T 1J4 Canada The Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany The Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-shape network featured by incorporating the inception-like module into the encoder and adopting a contextual bridge for better feature fusion. The design proposed in this work is based on three core principles: (1) The patch merging module in the encoder is redesigned with ResInception Patch Merging (RIPM). Multi-branch transformer (MB transformer) adopts the same number of branches as the outputs of RIPM. Combining the two modules enables the model to capture a multi-scale representation within a single stage. (2) We construct an Intra-stage Feature Fusion (IFF) module following the MB transformer to enhance the aggregation of feature maps from all the branches and particularly focus on the interaction between the different channels of all the scales. (3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective. Extensive experiments on multi-organ and skin lesion segmentation tasks present the superior performance of TransCeption compared to previous work. The code is publicly available on GitHub. © 2023, CC BY.

关键词： Merging

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Event-triggered Multi-agent Optimal Regulation Using Adaptive Dynamic programming

Event-triggered Multi-agent Optimal Regulation Using Adaptiv...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiangnan Zhong Haibo He Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

This paper develops an event-triggered multi-agent control method based on adaptive dynamic programming (ADP) techniques. Different from the traditional ADP-based multi-agent control with fixed sampling period, our method designs an adaptive controller only based on the efficiently reduced samples. The sampling instants are decided by an adaptive triggering condition to guarantee the stability of the event-triggered learning process. The theoretical analysis of the proposed method is also provided in this paper. It is proved that the designed event-triggered ADP controller can make all the agents synchronize to the leader's dynamics with reduced sampled data, and also reach Nash equilibrium at the same time. Therefore, the proposed method can save the computational resources in the learning process. Finally, the simulation results verify the theoretical analysis and also demonstrate the performance of the developed method.

关键词： Multi-agent systems Stability analysis Nash equilibrium Dynamic programming Synchronization Task analysis Nickel

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Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope

Combining multiple contrasts for improving machine learning-...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mercy N. Asiedu Erica Skerrett Guillermo Sapiro Nirmala Ramanujam Department of Biomedical Engineering and Department of Global Health Duke University Durham NC USA Department of Biomedical Engineering and Department of Electrical Engineering and Computer Science Duke University Durham NC USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

We apply feature-extraction and machine learning methods to multiple sources of contrast (acetic acid, Lugol's iodine and green light) from the white Pocket Colposcope, a low-cost point of care colposcope for cervical cancer screening. We combine features from the sources of contrast and analyze diagnostic improvements with addition of each contrast. We find that overall AUC increases with additional contrast agents compared to using only one source.

关键词： Feature extraction Classification algorithms Image segmentation Machine learning Cervical cancer Image color analysis

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Hybridization of colloidal handlebodies with singular defects and topological solitons in chiral liquid crystals

arXiv

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arXiv 2025年

作者： Lee, Jun-Yong Kumari, Asha Yuan, Ye Tasinkevych, Mykola Smalyukh, Ivan I. International Institute for Sustainability with Knotted Chiral Meta Matter Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Department of Physics and Mathematics School of Science and Technology Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program and Soft Materials Research Center University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States

Topology can manifest itself in colloids when quantified by invariants like Euler characteristics of nonzero-genus colloidal surfaces, albeit spherical colloidal particles are most often studied, and colloidal particles with complex topology are rarely considered. On the other hand, singular defects and topological solitons often define the physical behavior of the molecular alignment director fields in liquid crystals. Interestingly, nematic liquid crystalline dispersions of colloidal particles allow for probing the interplay between topologies of surfaces and fields, but only a limited number of such cases have been explored so far. Here, we study the hybridization of topological solitons, singular defects, and topologically nontrivial colloidal particles with the genus of surfaces different from zero in a chiral nematic liquid crystal phase. Hybridization occurs when distortions separately induced by colloidal particles and LC solitons overlap, leading to energy minimization-driven redistribution of director field deformations and defects. As a result, hybrid director configurations emerge, combining topological features from both components. We uncover a host of director field configurations complying with topological theorems, which can be controlled by applying electric fields. Rotational and translational dynamics arise due to the nonreciprocal evolution of the director fields in response to alternating electric fields of different frequencies. These findings help define a platform for controlling topologically hyper-complex colloidal structures and dynamics with electrically reconfigurable singular defects and topological solitons induced by colloidal handlebodies. Copyright © 2025, The Authors. All rights reserved.

关键词： Topology

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Fabrication of a Translational Photoacoustic Needle Sensing Probe

SSRN

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SSRN 2022年

作者： Lin, Wei-Kuan Ni, Linyu Wang, Xueding Guo, Jay L. Xu, Guan Department of Electrical Engineering and Computer Sciences University of Michigan 1301 Beal Avenue Ann ArborMI United States Department of Biomedical Engineering University of Michigan 2200 Bonisteel Blvd Ann ArborMI United States Department of Radiology University of Michigan 1301 Catherine St Ann ArborMI United States Department of Ophthalmology and Visual Sciences University of Michigan 1000 Wall St Ann ArborMI United States

In our previous study, we demonstrated the feasibility of using an all-optical photoacoustic (PA) probe for interstitial measurement of tissue architectures. In this work, we fabricated a translational needle PA sensing probe that integrates a fiber optic diffuser and a fiber optic hydrophone into an 18G steel biopsy needle sheath. We upgraded the Fabry-Perot hydrophone from the previous planar structure to plano-concave structure to boost the sensitivity and the sensing angle. We also improved the needle sheath by laying a polymer layer and a highly reflective metal layer on the inner surface of the needle. Such design effectively diminishes the vibrational noise from the sheath to a negligible degree and increases the sensing volume. We also validated the probe for PA spectral analysis with phantoms containing microspheres. The correlations between the quantitative PA spectral parameters agree with our previous results. © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

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Quantifying the impact of scattering on the point spread function using a Modified Full Field Optical Coherence Tomography Setup

Quantifying the impact of scattering on the point spread fun...

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Clinical and Translational Biophotonics, Translational 2020

作者： Zilpelwar, Sharvari Postnov, Dmitry Chen, Anderson Ichun Cheng, Xiaojun Tian, Lei Devor, Anna Boas, David A. 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 Department of Neurosciences and Radiology University of California San DiegoCA92093 United States

We present the preliminary results of our depth-resolved PSF measurement in static scattering phantoms. This forms a basis for correcting the degradation of the point spread function by optical scattering. © OSA ... 详细信息

ISBN: (纸本)9781943580743

关键词： Optical transfer function

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Vision Transformer for Parkinson’s Disease Classification using Multilingual Sustained Vowel Recordings

Vision Transformer for Parkinson’s Disease Classification u...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Daria Hemmerling Marek Wodzinski Juan Rafael Orozco-Arroyave David Sztaho Mateusz Daniol Pawel Jemiolo Magdalena Wojcik-Pedziwiatr Daria Hemmerling Marek Wodzinski Mateusz Daniol and Pawel Jemiolo are With the AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland Information Systems Institute University of Applied Sciences Western Switzerland (HES-SO Valais) Sierre Switzerland Universidad de Antioquia Medellin Colombia and With the Pattern Recognition Lab at the University of Erlangen Erlangen Germany Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Hungary Department of Neurology Andrzej Frycz Modrzewski Krakow University Krakow Poland

Parkinson’s disease (PD) is the 2 nd most prevalent neurodegenerative disease in the world. Thus, the early detection of PD has recently been the subject of several scientific and commercial studies. In this paper, we propose a pipeline using Vision Transformer applied to mel-spectrograms for PD classification using multilingual sustained vowel recordings. Furthermore, our proposed transformed-based model shows a great potential to use voice as a single modality biomarker for automatic PD detection without language restrictions, a wide range of vowels, with an F1-score equal to 0.78. The results of our study fall within the range of the estimated prevalence of voice and speech disorders in Parkinson’s disease, which ranges from 70-90%. Our study demonstrates a high potential for adaptation in clinical decision-making, allowing for increasingly systematic and fast diagnosis of PD with the potential for use in *** relevance— There is an urgent need to develop non invasive biomarker of Parkinson’s disease effective enough to detect the onset of the disease to introduce neuroprotective treatment at the earliest stage possible and to follow the results of that intervention. Voice disorders in PD are very frequent and are expected to be utilized as an early diagnostic biomarker. The voice analysis using deep neural networks open new opportunities to assess neurodegenerative diseases’ symptoms, for fast diagnosis-making, to guide treatment initiation, and risk prediction. The detection accuracy for voice biomarkers according to our method reached close to the maximum achievable value.

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