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A dual-mode deep transfer learning (D2TL) system for breast cancer detection using contrast enhanced digital mammograms

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IISE Transactions on Healthcare systems engineering 2019年第4期9卷 357-370页

作者： Wang, Kun Patel, Bhavika K. Wang, Lujia Wu, Teresa Zheng, Bin Li, Jing Industrial Engineering Program at School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States Department of Radiology Mayo Clinic in Arizona Scottsdale United States School of Electrical and Computer Engineering College of Engineering University of Oklahoma Norman United States

Full-field digital mammography (FFDM) and magnetic resonance imaging (MRI) are gold-standard techniques for breast cancer detection. The newly contrast-enhanced digital mammography (CEDM) integrates the complementary strengths of FFDM and MRI, and is being incorporated into the practice of leading institutions. The current clinical practice using CEDM is sub-optimal because it is primarily based on clinicians' trained eyes. Automated diagnostic systems under the conventional machine learning paradigm suffer from drawbacks, such as the requirement for precise segmentation, extraction of shallow features that do not suffice for diagnostic images, and adoption of a sequential design without a global objective. We propose a deep learning (DL)-empowered diagnostic system using CEDM, the core of which is a novel dual-mode deep transfer learning (D2TL) model. The proposed system is innovative in several aspects, including (1) a dual-mode deep architecture design;(2) use of transfer learning to facilitate robust model estimation under small sample-size;(3) development of visualization techniques to help interpret the model results and facilitate inter- and intra-tumor malignancy quantification;and (4) minimization of human bias. We apply D2TL to classify benign vs. malignant tumors using the CEDM data collected from the Mayo Clinic in Arizona. D2TL outperforms competing models and approaches. © 2019, © 2019 “IISE”.

关键词： breast cancer Deep learning imaging-based diagnosis transfer learning

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Design of an enhanced disc golf game to facilitate players with visual impairments

Design of an enhanced disc golf game to facilitate players w...

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AHFE 2018 International Conference on Social and Occupational Ergonomics, 2018

作者： Glattke, Kaycee Fakhri, Bijan Heath, Corey Moore, Meredith Rahimi, Marziye School of Biological and Health Systems Engineering Arizona State University Tempe United States School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States School for Engineering of Matter Transport and Energy Arizona State University Tempe United States

ISBN: (纸本)9783319939995

Participation in recreational activities is important to maintaining a healthy lifestyle;however, obesity rates are climbing, particularly among individuals with visual impairments (VI). One of the contributing factors to the high obesity rates is the lack of accessible physical activities available for people with disabilities to participate in. Many of the current activities suggested to individuals with VI require teams and organization, and do not support inclusion between individuals with VI and those without. One solution to this is to create a mobile system for disc golf that facilitates accessibility for individuals with VI. Disc golf has become increasingly popular since its inception in the 1960s, and much of its popularity is likely due to its accessibility;with limited required equipment, free public courses, and a gentle learning curve for beginners, disc golf is an easy activity to become involved in. It would be an ideal activity to address physical activity and social needs for individuals with VI;however, the game is highly dependent on visual cues for locating goal points, avoiding obstacles, and finding one’s disc. This project aims to extend accessibility of disc golf by designing a theoretical auditory notification and GPS mapping system. This system is intended to be integrated into existing disc golf courses without affecting how the game is currently played, and its implementation would allow both individuals with and without VI to play the game simultaneously with one another. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Golf

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Leveraging Machine Learning for Gait Phase Classification with Varied Training Methods

Leveraging Machine Learning for Gait Phase Classification wi...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Amal Mekni Jyotindra Narayan Hassène Gritli Higher Institute of Information and Communication Technologies University of Carthage RISC Laboratory (LR16ES07) Borj Cedria Tunis Tunisia Department of Computing Imperial College London UK Chair of Digital Health Universität Bayreuth Germany Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Accurate gait phase recognition is crucial for real-time analysis and intervention in rehabilitation, biomechanics, and prosthetics. However, achieving this is challenging due to the diverse machine learning (ML) training methods. This study employs ML algorithms to classify gait phases, focusing on stance and swing phases, utilizing open-source data from $\mathbf{1 0 0}$ participants ($41.91 \pm 5.3$ yrs). The classification algorithms considered are k-Nearest Neighbor (k-NN), Logistic Regression (LR), decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayesian (NB) algorithms. The study evaluates these algorithm performances using two training methods with five and ten lower body movements: one randomly selects $80 \%$ of stance and swing phase data for training, while the other divides data by participants, allocating $80 \%$ for training and $20 \%$ for testing. When assessing accuracy with five movements, RF achieved $99.8 \%$ for both training methods. With ten movements, RF achieved a high accuracy of $99.9 \%$ using the second method. Notably, the performance of all ML algorithms exhibited improvement when considering data from ten movements versus five. Additionally, it was observed that the second training method proved more effective with five-movement data compared to data involving ten movements. This comprehensive evaluation highlights the potential of machine learning algorithms for accurate gait phase recognition in diverse applications with varied training methods.

关键词： Training Radio frequency Support vector machines Machine learning algorithms Accuracy Real-time systems Classification algorithms

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Discovering patterns in the topography of existing settlements: the case of the China-Pakistan Economic Corridor (CPEC) route

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Arabian Journal of Geosciences 2022年第1期16卷 1-14页

作者： Maqsoom, Ahsen Aslam, Bilal Khalil, Umer Azam, Sheheryar Kazmi, Zaheer Abbas Rana, Muhammad Usman Arif Department of Civil Engineering COMSATS University Islamabad Wah Campus Pakistan School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff USA ITC Faculty of Geo-Information Science and Earth Observation University of Twente Enschede The Netherlands Department of Engineering and Sciences University of Greenwich at Medway Gillingham UK Department of Civil and Construction Engineering Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Management Sciences Riphah International University Islamabad Pakistan

It is important to recognize the influence of terrain characteristics on the spatial distribution of settlements as it can aid policymakers in sustainable urban planning. Landscape characteristics influence the decision of new settlements as well as the decision-making process related to existing settlement density. The surrounding environment influences human activities, and this can be examined with the help of research targeting the landscape characteristics and their association with settlements. In this research, eight topographical factors, namely, elevation, slope, aspect, incision depth, surface roughness, curvature, and standard deviation of elevation, were analyzed using GIS along the China-Pakistan Economic Corridor (CPEC) route to assess the importance of these topographical factors regarding the settlement intensity. The idea of Distributive Entropy (DE) has been utilized to examine the topographical factors based on the extent of settlement distribution as the physical area characteristics directly affect the settlement dissemination. The quantitative analysis of DE yields higher and lower values and varies throughout the study area. The results showed that the settlements are centered mainly on low elevations and low slopes, especially in valleys. Results also showed that a sufficient amount of the study area is considered suitable for settlement dissemination and the unsuitable area is relatively less. Future settlement plans and schemes can use the outcomes of this study to transform the development and expansion into a more sustainable, controlled, and pro-environment.

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Research and Design Trends for Exam Room computing to Enhance Patient Centeredness

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021年第1期65卷 196 - 200页

作者： Jason J. Saleem Laura G. Militello Onur Asan Jacob M. Read Enid Montague Department of Industrial Engineering University of Louisville Louisville KY USA Applied Decision Science LLC Dayton OH USA School of Systems & Enterprises Stevens Institute of Technology Hoboken NJ USA College of Computing and Digital Media DePaul University Chicago IL USA

While there is an extensive and established history of research that demonstrates the unfortunate capacity of exam room computing and electronic health records (EHRs) to negatively impact provider-patient communication and interaction, recent trends in exam room computing are promising in that there may be an opportunity for the EHR to improve patient engagement. The logical evolution of this research is to flip the narrative to understand strategies for using exam room computing as a mediator or facilitator of provider-patient communication and interaction, rather than simply establishing ways to mitigate the documented barriers. Panelists will present and discuss their latest research and experiences that may contribute to the evolution of using exam room computing as a tool to enhance provider-patient communication and interaction. We will also discuss how a thoughtful exam room design with patient-centered exam room computing technologies and practices may positively impact specific human factors, safety, and bias outcomes.

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Otfpf: Optimal Transport Based Feature Pyramid Fusion Network for Brain Age Estimation

SSRN

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SSRN 2022年

作者： Fu, Yu Huang, Yanyan Zhang, Zhe Dong, Shunjie Xue, Le Niu, Meng Li, Yunxin Shi, Zhiguo Wang, Yalin Zhang, Hong Tian, Mei Zhuo, Cheng College of Information Science & Electronic Engineering Zhejiang University Hangzhou China Binjiang Institute Zhejiang University Hangzhou China School of Physics Hangzhou Normal University Hangzhou China Institute of Brain Science Hangzhou Normal University Hangzhou China Department of Nuclear Medicine Medical PET Center The Second Hospital of Zhejiang University School of Medicine Hangzhou China Department of Radiology The First Hospital of Lanzhou University Lanzhou China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Human Phenome Institute Fudan University Shanghai China

Deep neural networks can be used to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could be a useful biomarker for detecting development-related and aging-related disorders. Here, we propose the optimal transport based feature pyramid fusion (OTFPF) network for brain age estimation using T1 MRIs. The Optimal Transport based Feature Pyramid Fusion (OTFPF) module, the 3D overlapped ConvNeXt (3D OL-ConvNeXt) module, and the fusion module make up the OTFPF network. These modules not only improve the OTFPF network's comprehension of each brain's semi-multimodal and multi-level feature pyramid information, but also significantly enhance its understanding of brain development and aging. In comparison to recent state-of-the-art models, the proposed OTFPF network converges more quickly, performs better, and is easy to interpret. Experiments involving 12,909 MRIs of individuals aged 3-97 years reveal that the OTFPF network can accurately estimate brain age with a mean absolute error (MAE) of 1.846, Pearson's correlation coefficient (PCC) of 0.9941, and Spearman's rank correlation coefficient (SRCC) of 0.9802. Extensive quantitative experiments, ablation experiments, and dataset-scale evaluations demonstrate that the OTFPF network is superior, rational, and stable. According to the OTFPF network, the age-related heatmaps of the brain explain the biological mechanisms underlying brain aging. Finally, the OTFPF network is used to analyze datasets related to brain disorders, demonstrating its utility. © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance

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KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation

arXiv

引用

arXiv 2021年

作者： Khiarak, Jalil Nourmohammadi Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2 deep neural network method. We employ the pre-trained weights given with MobileNetV2 for the ImageNet dataset and fine-tune it on the iris recognition and localization domain. Further, we have introduced a new dataset, called KartalOl, to better evaluate detectors in iris recognition scenarios. To provide domain adaptation, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also augment the data by performing left-right flips, rotation, zoom, and brightness. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed method is tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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UML-based combat effectiveness simulation system modeling within MDE

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Journal of systems engineering and Electronics 2018年第6期29卷 1180-1196页

作者： ZHU Zhi LEI Yonglin SARJOUGHIAN Hessam LI Xiaobo ZHU Yifan Department of Military Modeling and Simulation School of Systems Engineering National University of Defense Technology Arizona Center for Integrated Modeling and Simulation School of Computing Informatics and Decision Systems EngineeringArizona State University

To reduce complexity, the combat effectiveness simulation system(CESS) is often decomposed into static structure,physical behavior, and cognitive behavior, and model abstraction is layered onto domain invariant knowledge(DIK) and application variant knowledge(AVK) levels. This study concentrates on the specification of CESS’s physical behaviors at the DIK level of abstraction, and proposes a model driven framework for efficiently developing simulation models within model-driven engineering(MDE). Technically, this framework integrates the four-layer metamodeling architecture and a set of model transformation techniques with the objective of reducing model heterogeneity and enhancing model continuity. As a proof of concept, a torpedo example is illustrated to explain how physical models are developed following the proposed framework. Finally, a combat scenario is constructed to demonstrate the availability, and a further verification is shown by a reasonable agreement between simulation results and field observations.

关键词： domain specific modeling model-driven development system engineering effectiveness simulation

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Convolutional neural network regression for low-cost microalgal density estimation

TechRxiv

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TechRxiv 2022年

作者： Nguyen, Linh Nguyen, Dung K. Nghiem, Truong X. Nguyen, Thang Institute of Innovation Science and Sustainability Federation University Australia ChurchillVIC3842 Australia College of Agriculture and Science Life Chonnam National University Buk-gu Gwangju61186 Korea Republic of School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States School of Engineering & Computing Sciences Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States

Density of microalgae is critical information for production of algae in a closed cultivation system since it can be used to optimally control their growth rate, biomass concentration and quality of the products. Given advancement in image processing techniques and thanks to low-cost camera sensors, image based methods are increasingly widely utilized to indirectly estimate the density. Advantages of the image based techniques include being less invasive and more nondestructive and biosecured. However, most of the existing techniques rely on averaging all pixels of a microalgae image, which may eliminate crucial information of their spatial correlation. Therefore, in this work we propose to exploit a convolutional neural network (CNN) to efficiently extract information from the microalgae images, which are then employed to regress the density. Interestingly, the proposed deep CNN regression model accepts the whole color image as its input while the density is calculated in the output. The proposed CNN regression architecture was validated in real-world experiments where the microalgal strain Chlorella vulgaris was cultured and their images were captured by our low-cost camera sensor system. The obtained results demonstrate that the averaged estimation accuracy of the proposed model is 99.45% (± 0.68%) while the R2 value between the density predictions and the ground truths is 0.9997, which is highly accurate and practical. © 2022, CC BY.

关键词： Microalgae

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Probabilistic multimodal modeling for human-robot interaction tasks

arXiv

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arXiv 2019年

作者： Campbell, Joseph Stepputtis, Simon Amor, Heni Ben School of Computing Informatics and Decision Systems Engineering Arizona State University

Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy. Despite this observation, few HRI methods are capable of efficiently performing inference for multimodal systems. In this work, we introduce a reformulation of Interaction Primitives which allows for learning from demonstration of interaction tasks, while also gracefully handling nonlinearities inherent to multimodal inference in such scenarios. We also empirically show that our method results in more accurate, more robust, and faster inference than standard Interaction Primitives and other common methods in challenging HRI scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Human robot interaction

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