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Optical Elastography and Tissue Biomechanics VIII 2021

作者： Kling, Ami Kirkpatrick, Sean J. Jiang, Jingfen Department of Biomedical Engineering Health Research Institute Institute of Computing and Cybersystems Michigan Technological University HoughtonMI49931 United States Center of Biocomputing and Digital Health Health Research Institute Institute of Computing and Cybersystems Michigan Technological University HoughtonMI49931 United States

ISBN: (数字)9781510641266

ISBN: (纸本)9781510641259

Techniques aimed at the non-invasive characterization of soft tissues according to elastic properties are rapidly evolving. Virtual touch-based elastographic methods including acoustic radiation force imaging (ARFI) and optical elastography measure the peak axial displacement (PD) and time-to-peak-displacement (TTP) of tissue in response to a localized force. These measurements have been used clinically to differentiate tissues, albeit with mixed results. However, to date, the reason has not been fully understood. In this study, we apply a novel modeling approach to explore the mechanistic link between simplistic displacement measurements and tissue viscoelasticity in the application of virtual touch-based elastographic methods to staging chronic liver disease (CLD). To our knowledge, such a study has not been reported in the literature. Specifically, a numerical screening study was first conducted to identify factors that most strongly determine PD and TTP. Response surface experimental designs were then applied to these factors to produce meta-models of expected PD and TTP probability density functions (PDFs) as functions of identified factors. Results from the screening study suggest that both PD and TTP measurements are primarily influenced by three factors: the initial Young’s modulus of the tissue, the first viscoelastic Prony series time constant, and pre-compression applied during acquisition. To investigate the implications of these results, stochastic inputs for these three factors associated were used to determine a robust response surface. The identified response surface methodology can be used to determine optimal cutoff values for PD and TTP that could be used in order to stage chronic liver disease. © 2021 SPIE

关键词： Tissue

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νGAN: A Generative Adversarial Emulator for Cosmic Web with Neutrinos

arXiv

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arXiv 2025年

作者： Kaushal, Neerav Giusarma, Elena Reyes, Mauricio Institute of Computing and Cybersystems Michigan Technological University HoughtonMI49931 United States Department of Physics Michigan Technological University HoughtonMI49931 United States

Understanding the impact of neutrino masses on the evolution of Universe is a crucial aspect of modern cosmology. Due to their large free streaming lengths, neutrinos significantly influence the formation of cosmic structures at non-linear scales. To maximize the information yield from current and future galaxy surveys, it is essential to generate precise theoretical predictions of structure formation. One approach to achieve this is by running large sets of cosmological numerical simulations, which is a computationally intensive process. In this study, we propose a deep learning-based generative adversarial network (GAN) model to emulate the Universe for a variety of neutrino masses. Our model called νGAN (for neutrino GAN) is able to generate 2D cosmic webs of the Universe for a number of neutrino masses ranging from 0.0 eV to 0.4 eV. The generated maps exhibit statistical independence, lack correlations with training data, and very closely resemble the distribution of matter in true maps. We assess the accuracy of our results both visually and through key statistics used in cosmology and computer vision analyses. Our results indicate that samples generated by νGAN are accurate within a 5% error on power spectrum between k = 0.01 to k = 0.5 hMpc−1. Although this accuracy covers the mildly non-linear scales, consistent with other works and observations, achieving higher accuracy at fully non-linear scales requires more sophisticated models, such as diffusion models. Nevertheless, our work opens up new avenues for building emulators to generate fast and massive neutrino simulations, potentially revolutionizing cosmological predictions and analyses. This work serves as a proof-of-concept, paving the way for future extensions with higher-resolution 3D data and advanced generative models. © 2025, CC BY.

关键词： Generative adversarial networks

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Comparing mouse versus trackpad input in a web-based app for assessing motor learning 67th

Comparing mouse versus trackpad input in a web-based app for...

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67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023

作者： Watral, Alexandra T. Morley, Abby Pastel, Robert Trewartha, Kevin M. Department of Cognitive & Learning Sciences Michigan Technological University HoughtonMI United States Health Research Institute Michigan Technological University HoughtonMI United States Department of Computer Science Michigan Technological University HoughtonMI United States Institute of Computing and Cybersystems Michigan Technological University HoughtonMI United States Department of Kinesiology & Integrative Physiology Michigan Technological University HoughtonMI United States

Current laboratory approaches to measuring motor learning are not accessible to all populations, limiting research about developmental processes and medical conditions that impact motor control. We recently created a web-based application remote assessment of visuomotor adaptation, a gold-standard approach to studying motor learning. Previously, we validated this application in younger and older adults. However, preliminary analyses suggested that the input device (mouse or trackpad) may have impacted performance. The current study directly evaluated performance differences in younger adults using the application with a mouse compared to a trackpad. Results showed no statistically significant differences in learning curves or movement times between groups, but reaction time was significantly faster in mouse users. While the input device had very little impact on motor learning, slower reaction time when using a trackpad may be related to increased cognitive demands or reduced movement efficiency compared to using a mouse for this task. © 2023 Human Factors and Ergonomics Society.

关键词： Input device Motor learning Remote testing Web application

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Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection

SSRN

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SSRN 2025年

作者： Awad, Ali Saleem, Ashraf Paheding, Sidike Lucas, Evan Al-Ratrout, Serein Havens, Timothy C. Department of Applied Computing College of Computing Michigan Technological University 1400 Townsend Dr Houghton49931 United States Department of Computer Science and Engineering Fairfield University FairfieldCT06824 United States Institute of Computing and Cybersystems Michigan Technological University Michigan Houghton49931 United States Department of Computer Science Michigan Technological University Michigan Houghton49931 United States

Underwater imagery often suffers from severe degradation, resulting in low visual quality and reduced object detection performance. This work aims to evaluate state-of-the-art image enhancement models, investigate their effects on underwater object detection, and explore their potential to improve detection performance. To this end, we apply nine recent underwater image enhancement models, covering physical, non-physical, and learning-based categories, to two recent underwater image datasets. Following this, we conduct joint qualitative and quantitative analyses on the original and enhanced images, revealing the discrepancy between the two analyses, and analyzing changes in the quality distribution of the images after enhancement. We then train three recent object detection models on the original datasets, selecting the best-performing detector for further analysis. This detector is subsequently re-trained on the enhanced datasets to evaluate changes in detection performance, highlighting the adverse effect of enhancement on detection performance at the dataset level. Next, we perform a correlation study to examine the relationship between various enhancement metrics and the mean Average Precision (mAP). Finally, we conduct an image-level analysis that reveals images of improved detection performance after enhancement. The findings of this study demonstrate the potential of image enhancement to improve detection performance and provide valuable insights for researchers to further explore the effects of enhancement on detection at the individual image level, rather than at the dataset level. This could enable the selective application of enhancement for improved detection. The data generated, code developed, and supplementary materials are publicly available at: https://***/RSSL-MTU/Enhancement-Detection-Analysis. © 2025, The Authors. All rights reserved.

关键词： Underwater photography

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Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection

arXiv

引用

arXiv 2024年

作者： Awad, Ali Saleem, Ashraf Paheding, Sidike Lucas, Evan Al-Ratrout, Serein Havens, Timothy C. Department of Applied Computing College of Computing Michigan Technological University 1400 Townsend Dr Houghton49931 United States Department of Computer Science and Engineering Fairfield University FairfieldCT06824 United States Institute of Computing and Cybersystems Michigan Technological University HoughtonMI49931 United States Department of Computer Science Michigan Technological University HoughtonMI49931 United States

Underwater imagery often suffers from severe degradation resulting in low visual quality and reduced object detection performance. This work aims to evaluate state-of-the-art image enhancement models, investigate their effects on underwater object detection, and explore their potential to improve detection performance. To this end, we apply nine recent underwater image enhancement models, covering physical, non-physical and learning-based categories, to two recent underwater image datasets. Following this, we conduct joint qualitative and quantitative analyses on the original and enhanced images, revealing the discrepancy between the two analyses, and analyzing changes in the quality distribution of the images after enhancement. We then train three recent object detection models on the original datasets, selecting the best-performing detector for further analysis. This detector is subsequently re-trained on the enhanced datasets to evaluate changes in detection performance, highlighting the adverse effect of enhancement on detection performance at the dataset level. Next, we perform a correlation study to examine the relationship between various enhancement metrics and the mean Average Precision (mAP). Finally, we conduct an image-level analysis that reveals images of improved detection performance after enhancement. The findings of this study demonstrate the potential of image enhancement to improve detection performance and provide valuable insights for researchers to further explore the effects of enhancement on detection at the individual image level, rather than at the dataset level. This could enable the selective application of enhancement for improved detection. The data generated, code developed, and supplementary materials are publicly available at: https://***/RSSL-MTU/Enhancement-Detection-Analysis. © 2024, CC BY.

关键词： Remote sensing

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Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography

arXiv

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arXiv 2025年

作者： Yao, Ni Liu, Xiangyu Sun, Danyang Han, Chuang Li, Yanting Nan, Jiaofen Li, Chengyang Zhu, Fubao Zhou, Weihua Zhao, Chen School of Computer Science and Technology Zhengzhou University of Light Industry Henan Zhengzhou450001 China School of Information Management and Engineering Shanghai University of Finance and Economics China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States Department of Computer Science Kennesaw State University MariettaGA United States

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://***/chenzhao2023/MGFA_CCTA. © 2025, CC BY.

关键词： Image segmentation

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3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention

arXiv

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arXiv 2024年

作者： Huang, Huan Qiu, Liheng Yang, Shenmiao Li, Longxi Nan, Jiaofen Li, Yanting Han, Chuang Zhu, Fubao Zhao, Chen Zhou, Weihua School of Computer Science and Technology Zhengzhou University of Light Industry Henan Zhengzhou450002 China Peking University Peoples’ Hospital Peking University Institute of Hematology Beijing China Department of Nuclear Medicine Peking University Peoples’ Hospital Beijing China Department of Computer Science Kennesaw State University MariettaGA United States Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Background: Accurate segmentation of diffuse large B-cell lymphoma (DLBCL) lesions is challenging due to their complex patterns in medical imaging. Objective: This study aims to develop a precise segmentation method for DLBCL using 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images. Methods: We propose a 3D dual-branch encoder segmentation method using shifted window transformers and a Multi-Scale Information Fusion (MSIF) module. To enhance feature integration, the MSIF module performs multi-scale feature fusion using cross-attention mechanisms with a shifted window framework. A gated neural network within the MSIF module dynamically balances the contributions from each modality. The model was optimized using the Dice Similarity Coefficient (DSC) loss function. Additionally, we computed the total metabolic tumor volume (TMTV) and performed statistical analyses. Results: The model was trained and validated on a dataset of 165 DLBCL patients using 5-fold cross-validation, achieving a DSC of 0.7512. Statistical analysis showed a significant improvement over comparative methods (p 2 of 0.89 were observed when comparing manual annotations to segmentation results for TMTV measurement. Conclusion: This study presents an effective automatic segmentation method for DLBCL that leverages the complementary strengths of PET and CT imaging. Our method has the potential to improve diagnostic interpretations and assist in treatment planning for DLBCL patients. Copyright © 2024, The Authors. All rights reserved.

关键词： Positron emission tomography

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A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy

arXiv

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arXiv 2023年

作者： He, Zhuo Si, Hongjin Zhang, Xinwei Chen, Qing-Hui Zou, Jiangang Zhou, Weihua Department of Applied Computing Michigan Technological University HoughtonMI United States Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Jiangsu Nanjing China Department of Cardiology The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University Jiangsu Huaian China Department of Kinesiology and Integrative Physiology HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Background: Cardiac resynchronization therapy (CRT) has emerged as an effective treatment for heart failure patients with electrical dyssynchrony. However, accurately predicting which patients will respond to CRT remains a challenge. This study explores the application of deep transfer learning techniques to train a predictive model for CRT response. Methods: In this study, the short-time Fourier transform (STFT) technique was employed to transform ECG signals into two-dimensional images. A transfer learning approach was then applied on the MIT-BIT ECG database to pre-train a convolutional neural network (CNN) model. The model was fine-tuned to extract relevant features from the ECG images, and then tested on our dataset of CRT patients to predict their response. Results: Seventy-one CRT patients were enrolled in this study. The transfer learning model achieved an accuracy of 72% in distinguishing responders from non-responders in the local dataset. Furthermore, the model showed good sensitivity (0.78) and specificity (0.79) in identifying CRT responders. The performance of our model outperformed clinic guidelines and traditional machine learning approaches. Conclusion: The utilization of ECG images as input and leveraging the power of transfer learning allows for improved accuracy in identifying CRT responders. This approach offers potential for enhancing patient selection and improving outcomes of CRT. © 2023, CC BY.

关键词： Electrocardiograms

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Jensen-Shannon Divergence Based Novel Loss Functions for Bayesian Neural Networks

arXiv

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arXiv 2022年

作者： Thiagarajan, Ponkrshnan Ghosh, Susanta Hopkins Extreme Materials Institute Johns Hopkins University MD United States Department of Mechanical Engineering-Engineering Mechanics Michigan Technological University HoughtonMI United States The Center for Artificial Intelligence The Institute of Computing and Cybersystems The Center for Applied Mathematics and Statistics Michigan Technological University HoughtonMI United States

Bayesian neural networks (BNNs) are state-of-the-art machine learning methods that can naturally regularize and systematically quantify uncertainties using their stochastic parameters. Kullback-Leibler (KL) divergence-based variational inference used in BNNs suffer from unstable optimization and challenges in approximating light-tailed posteriors due to the unbounded nature of the KL divergence. To resolve these issues, we formulate a novel loss function for BNNs based on a new modification to the generalized Jensen-Shannon (JS) divergence, which is bounded. In addition, we propose a Geometric JS divergence-based loss, which is computationally efficient since it can be evaluated analytically. We found that the JS divergence-based variational inference is intractable, and hence employed a constrained optimization framework to formulate these losses. Our theoretical analysis and empirical experiments on multiple regression and classification data sets suggest that the proposed losses perform better than the KL divergence-based loss, especially when the data sets are noisy or biased. Specifically, there are approximately 5% and 8% improvements in accuracy for a noise-added CIFAR-10 dataset and a regression dataset, respectively. There is about 13% reduction in false negative predictions of a biased histopathology dataset. In addition, we quantify and compare the uncertainty metrics for the regression and classification tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Constrained optimization

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SGUQ: Staged Graph Convolution Neural Network for Alzheimer’s Disease Diagnosis using Multi-Omics Data

arXiv

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arXiv 2024年

作者： Tao, Liang Xie, Yixin Deng, Jeffrey D. Shen, Hui Deng, Hong-Wen Zhou, Weihua Zhao, Chen Department of Computer Science Kennesaw State University MariettaGA30060 United States Department of Information Technology Kennesaw State University MariettaGA30060 United States Geisel School of Medicine Dartmouth College Hamover NH03755 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Applied Computing Michigan Technological University HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and burden worldwide. The advent of high-throughput omics technologies, such as genomics, transcriptomics, proteomics, and epigenomics, has revolutionized the molecular understanding of AD. Conventional AI approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis, which are inefficient and may be unnecessary. To reduce the clinical cost and improve the accuracy of AD diagnosis using multi-omics data, we propose a novel staged graph convolutional network with uncertainty quantification (SGUQ). SGUQ begins with mRNA and progressively incorporates DNA methylation and miRNA data only when necessary, reducing overall costs and exposure to harmful tests. Experimental results indicate that 46.23% of the samples can be reliably predicted using only single-modal omics data (mRNA), while an additional 16.04% of the samples can achieve reliable predictions when combining two omics data types (mRNA + DNA methylation). In addition, the proposed staged SGUQ achieved an accuracy of 0.858 on ROSMAP dataset, which outperformed existing methods significantly. The proposed SGUQ can not only be applied to AD diagnosis using multi-omics data, but also has the potential for clinical decision making using multi-viewed data. Our implementation is publicly available at https://***/chenzhao2023/multiomicsuncertainty. © 2024, CC BY.

关键词： Convolutional neural networks

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