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Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric

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arXiv 2020年

作者： Chen, Ruidi Paschalidis, Ioannis Ch. Division of Systems Engineering Boston University BostonMA02215 United States Department of Electrical and Computer Engineering Division of Systems Engineering Department of Biomedical Engineering Boston University BostonMA02215 United States

We develop Distributionally Robust Optimization (DRO) formulations for Multivariate Linear Regression (MLR) and Multiclass Logistic Regression (MLG) when both the covariates and responses/labels may be contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of distributions that are close to the empirical distribution of the training set in the sense of the Wasserstein metric. We relax the DRO formulation into a regularized learning problem whose regularizer is a norm of the coefficient matrix. We establish out-of-sample performance guarantees for the solutions to our model, offering insights on the role of the regularizer in controlling the prediction error. Experimental results show that our approach improves the predictive error by 7% – 37% for MLR, and a metric of robustness by 100% for MLG. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

High-Resolution Three-Dimensional Hybrid MRI + Low Dose CT Vocal Tract Modeling: A Cadaveric Pilot Study

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Journal of Voice 2022年

作者： MEYER, David RUSHO, Rushdi Zahid ALAM, Wahidul CHRISTENSEN, Gary E. HOWARD, David M. ATHA, Jarron HOFFMAN, Eric A. STORY, Brad TITZE, Ingo R. LINGALA, Sajan Goud Janette Ogg Voice Research Center Shenandoah University Winchester Virginia United States Roy J Carver Department of Biomedical Engineering University of Iowa Iowa City Iowa United States Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa United States Department of Electronic Engineering Royal Holloway University of London LDN United Kingdom Departments of Radiology Medicine and Roy J Carver Department of Biomedical Engineering University of Iowa Iowa City Iowa United States College of Science University of Arizona Tucson AZ United States Utah Center for Vocology The University of Utah Salt Lake City UT United States

Objectives: MRI based vocal tract models have many applications in voice research and education. These models do not adequately capture bony structures (e.g. teeth, mandible), and spatial resolution is often relatively low in order to minimize scanning time. Most MRI sequences achieve 3D vocal tract coverage at gross resolutions of 2 mm³ within a scan time of <20 seconds. Computed tomography (CT) is well suited for vocal tract imaging, but is infrequently used due to the risk of ionizing radiation. In this cadaveric study, a single, extremely low-dose CT scan of the bony structures is blended with accelerated high-resolution (1 mm³) MRI scans of the soft tissues, creating a high-resolution hybrid CT-MRI vocal tract model. Methods: Minimum CT dosages were determined and a custom 16-channel airway receiver coil for accelerated high (1 mm³) resolution MRI was evaluated. A rigid body landmark based partial volume registration scheme was then applied to the images, creating a hybrid CT-MRI model that was segmented in Slicer. Results: Ultra-low dose CT produced images with sufficient quality to clearly visualize the bone, and exposed the cadaver to 0.06 mSv. This is comparable to atmospheric exposures during a round trip transatlantic flight. The custom 16-channel vocal tract coil produced acceptable image quality at 1 mm³ resolution when reconstructed from ∼6 fold undersampled data. High (1 mm³) resolution MR imaging of short (<10 seconds) sustained sounds was achieved. The feasibility of hybrid CT-MRI vocal tract modeling was successfully demonstrated using the rigid body landmark based partial volume registration scheme. Segmentations of CT and hybrid CT-MRI images provided more detailed 3D representations of the vocal tract than 2 mm³ MRI based segmentations. Conclusions: The method described in this study indicates that high-resolution CT and MR image sets can be combined so that structures such as tee

关键词： Airway Computed Tomography(CT) Magnetic Resonance Imaging(MRI) Modeling Segmentation Vocal tract

Delay-Minimization and Back-Off Aware Q-Learning With Advanced Bio-Inspired CH Selection for Multi-Hop Communication in Vehicular Ad-Hoc Networks

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Radio Science 2025年第6期60卷

作者： Rashid, Sami Abduljabbar Audah, Lukman Hamdi, Mustafa Maad Abood, Mohammed Salah Abbas, Ghassan Raad Mohammed, Bassim Sayed Elwi, Taha A. Khan, Salahuddin Virdee, Bal S. Krasniqi, Astrit Kouhalvandi, Lida Alibakhshikenari, Mohammad Biomedical Engineering Research Center University of Anbar Ramadi Iraq Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Computer Science College of Computer Science and IT University of Anbar Ramadi Iraq School of Information and Electronics Beijing Institute of Technology Beijing China Institute of Mathematics and Information Technologies Volgograd State University Volgograd Russia Technical College of Engineering Al-Bayan University Baghdad Iraq Department of Automation and Artificial Intelligence Engineering College of Information Engineering at Al-Nahrain University Baghdad Iraq College of Engineering King Saud University Riyadh Saudi Arabia Center for Communications Technology London Metropolitan University London United Kingdom Department of Electrical and Electronics Engineering Dogus University Istanbul Turkey Electronics Engineering Department University of Rome "Tor Vergata" Rome Italy

The increasing significance of Vehicular Ad-hoc Networks (VANETs) in intelligent transportation systems has introduced challenges related to high mobility, network congestion, and energy efficiency. To address these challenges, this paper proposes a new approach based on Delay-Minimization and Back-Off Aware Q-Learning with Advanced Bio-Inspired Cluster Head (CH) Selection (DBACH) to enhance multi-hop data transmission in VANETs. The DBACH framework features network formation, latency minimization, a back-off Q-learning model, and an improved dragonfly algorithm-based CH selection. This method reduces transmission delay, routing overhead, and power consumption to enhance VANET QoS. DBACH was evaluated against RCDC, DCPA, and WCAM for effectiveness. The simulated vehicle numbers and speeds (km/h) were used to assess energy efficiency, throughput, packet delivery ratio, data loss ratio, computation time, and routing overhead. The DBACH boosts energy efficiency to 85 J, throughput to 160–200 Kbps, and packet delivery ratio to 11%–13%. Data loss drops to 7%–15%, latency is 60–94 ms, and routing overhead is 170–300 packets. When DBACH is a promising option for enhancing VANET communication dependability and energy economy due to its efficiency, communication stability, and success rates. © 2025. American Geophysical Union. All Rights Reserved.

关键词： Backpropagation

Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies

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arXiv 2022年

作者： Wang, Rongguang Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multi-study magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups, and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference etc. We find that models that incorporate multi-source data from demographic, clinical, genetic factors and cognitive scores are also unbiased. These models have better predictive AUC across subgroups than those trained only with imaging features but there are also situations when these additional features do not help. © 2022, CC BY.

关键词： Magnetic resonance imaging

Modelling Hybrid Acoustofluidic Devices for Enhancing Nano- and Mirco-Particle Manipulation in Microfluidics

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SSRN

SSRN 2022年

作者： Wang, Hanlin Yuan, Fan Xie, Zhihua Sun, Chao Wu, Fangda Mikhaylov, Roman Shen, Minghong Yang, Jian Zhou, You Liang, Dongfang Sun, Xianfang Wu, Zhenlin Yang, Zhiyong Yang, Xin Department of Electrical and Electronic Engineering School of Engineering Cardiff University CF24 3AA United Kingdom Department of Biomedical Engineering School of Engineering Duke University NC27708-0281 United States Department of Civil Engineering School of Engineering Cardiff University CF24 3AA United Kingdom School of Life Sciences Northwestern Polytechnical University 710072 China Division of Cancer and Genetics School of Medicine Cardiff University CF14 4XN United Kingdom Systems Immunity Research Institute School of Medicine Cardiff University CF14 4XN United Kingdom Department of Engineering University of Cambridge CB2 1PZ United Kingdom School of Computer Science Cardiff University CF24 3AA United Kingdom School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology 116023 China School of Mechanical Engineering Tianjin University 300072 China

Acoustofluidic techniques are increasingly used to manipulate nano- and micro-particles in microfluidics. A wide range of acoustofluidic devices integrating microchannel and acoustic sources have been developed for applications in biochemistry and biomedicine. In this work, we developed two novel acoustofluidic configurations: a microchannel was sandwiched between two identical surface acoustic wave (SAW) transducers (SAW-SAW), or between a piezoelectric transducer (PZT) and a SAW transducer (PZT-SAW). We numerically simulated the distributions of the acoustic pressure, time-averaged streaming velocity, and particle trajectories in these devices, and compared the simulation results among the different acoustofluidic configurations. The results revealed that both the SAW-SAW and PZT-SAW configurations produced significantly higher acoustic pressure and particle velocity in the microchannel © 2022, The Authors. All rights reserved.

关键词： Microfluidics

MT8 Cost-Consequences Analysis (CCA) of Echocardiography Digital Artificial Intelligence (AI) Monitoring Application Developed at Hosmartai (Horizon 2020 Funded)

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Value in Health 2024年第12期27卷 S488-S488页

作者： Chatzikou, M Latsou, D Apostolidis, G Charisis, V Hadjidimitriou, S Hadjileontiadis, L Pagkourelias, E Vassilikos, V Pharmecons Easy Access LtD York UK Signal Processing & Biomedical Technology Unit Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Biomedical Engineering Khalifa University Abu Dhabi Abu Dhabi United Arab Emirates 3rd Cardiology Department Hippokrateion Hospital Medical School Aristotle University of Thessaloniki Thessaloniki Greece

Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data

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arXiv 2021年

作者： Che, Hui Radbel, Jared Sunderram, Jag Nosher, John L. Patel, Vishal M. Hacihaliloglu, Ilker The Department of Biomedical Engineering Rutgers University NJ United States The Department of Medicine Rutgers Robert Wood Johnson Medical School NJ United States The Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States The Department of Electrical and Computer Engineering Johns Hopkins University MD United States The Department of Biomedical Engineering Rutgers University The Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States

The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools. However, difficulty in eliminating the risk of disease transmission, radiation exposure and not being cost-effective are some of the challenges for CT and CXR imaging. This fact induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due to its practical advantages of noninvasiveness, repeatability, and sensitive bedside property. In this paper, we utilize a deep learning model to perform the classification of COVID-19 from LUS data, which could produce objective diagnostic information for clinicians. Specifically, all LUS images are processed to obtain their corresponding local phase filtered images and radial symmetry transformed images before fed into the multi-scale residual convolutional neural network (CNN). Secondly, image combination as the input of the network is used to explore rich and reliable features. Feature fusion strategy at different levels is adopted to investigate the relationship between the depth of feature aggregation and the classification accuracy. Our proposed method is evaluated on the point-of-care US (POCUS) dataset together with the Italian COVID-19 Lung US database (ICLUS-DB) and shows promising performance for COVID-19 prediction. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions

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arXiv 2025年

作者： Ates, Gorkem Can Xin, Yu Gong, Kuang Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Biomedical Engineering University of Florida GainesvilleFL32611 United States Intelligent Clinical Care Center University of Florida GainesvilleFL32611 United States

Vision-language models (VLMs) have been widely applied to 2D medical image analysis due to their ability to align visual and textual representations. However, extending VLMs to 3D imaging remains computationally challenging. Existing 3D VLMs often rely on Vision Transformers (ViTs), which are computationally expensive due to the quadratic complexity of self-attention, or on 3D convolutions, which require large numbers of parameters and FLOPs as kernel size increases. We introduce DCFormer, an efficient 3D image encoder that factorizes 3D convolutions into three parallel 1D convolutions along the depth, height, and width dimensions. This design preserves spatial information while significantly reducing computational cost. Integrated into a CLIP-based vision-language framework, DCFormer is trained and evaluated on CT-RATE, a dataset of 50,188 paired 3D chest CT volumes and radiology reports. In zero-shot and fine-tuned detection of 18 pathologies, as well as in image–text retrieval tasks, DCFormer consistently outperforms state-of-the-art 3D vision encoders, including CT-ViT, ViT, ConvNeXt, PoolFormer, and TransUNet. These results highlight DCFormer’s potential for scalable, clinically deployable 3D medical VLMs. Our code is available at: https://***/mirthAI/DCFormer. © 2025, CC BY-NC-SA.

关键词： Three dimensional computer graphics

Verification of a Rapidly Multiplexed Circuit for Scalable Action Potential Recording

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Verification of a Rapidly Multiplexed Circuit for Scalable A...

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Mohit Sharma Hunter J. Strathman Ross M. Walker Dept. of Electrical and Computer Engineering University of Utah Salt Lake City USA Dept. of Biomedical Engineering University of Utah Salt Lake City USA

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This report presents characterizations of in vivo neural recordings performed with a CMOS multichannel chip that uses rapid multiplexing directly at the electrodes, without any pre-amplification or buffering. Neural recordings were taken from a 16-channel microwire array implanted in rodent cortex, with comparison to a gold-standard commercial bench-top recording system. We were able to record well-isolated threshold crossings from 10 multiplexed electrodes and typical local field potential waveforms from 16, with strong agreement with the standard system (average SNR = 2.59 and 3.07 respectively). For 10 electrodes, the circuit achieves an effective area per channel of 0.0077 mm 2 , which is >5× smaller than typical multichannel chips. Extensive characterizations of noise and signal quality are presented and compared to fundamental theory, as well as results from in vivo and in vitro experiments. By demonstrating the validation of rapid multiplexing directly at the electrodes, this report confirms it as a promising approach for reducing circuit area in massively-multichannel neural recording systems, which is crucial for scaling recording site density and achieving large-scale sensing of brain activity with high spatiotemporal resolution.

关键词： Electrodes Multiplexing In vivo Electric potential Action potentials biomedical engineering Urban areas