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IEEE International Geoscience and Remote Sensing Symposium

作者： Debasish Das Auroop Ganguly Snigdhansu Chatterjee Vipin Kumar Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Civil and Environmental Engineering Northeastern University School of Statistics University of Minnesota Computer Science and Engineering University of Minnesota

ISBN: (纸本)9781467311601

Discovery of dependence structure between precipitation extremes and other climate variables (covariates) within a smaller spatial and temporal neighborhood is an important step in better understanding the drivers of this complex phenomenon as well as short-term prediction of extremes occurrence. Apart from the inherent spatio-temporal variability of the dependence, it is further complicated by the availability of the covariates at different vertical levels. The above problem can be split into three different sub-problems. Firstly, a spatio-temporal neighborhood of influence has to be discovered, which can be different for different locations. Secondly, the dependence structure between the precipitation extremes and the covariates has to be discovered within this neighborhood and thirdly, it has to be investigated whether this dependence structure can be exploited for any predictive power. Climate scientists have already discovered some physics-based relations between some of the covariates (e.g. temperature, relative humidity, precipitable water etc.) and precipitation extremes. We are exploring data-dependent alternatives for these problems and any possibility of incorporating the physics-based relations into the resulting data model. In particular, we used elastic net-based sparse optimization technique which solves all three problems of neighborhood discovery, covariate dependence discovery and predictive modeling and at the same time maintains the interpretability of the resulting model. Preliminary results look promising and show potential for some interesting knowledge discovery. We are currently exploring non-linear correlations and the alternatives to combine the physics-based relationships into the data model.

关键词： One Two Three Four Five data models precipitation Chemical Precipitation precipitation (meteorology) extremum values Neighborhood DEPENDENCE data mining interpretability

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Antioxidant and antimicrobial activities of Saraca thaipingensis Cantley ex Prain

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亚太热带生物医学杂志（英文版） 2012年第S2期2卷 796-799页

作者： Supaluk Prachayasittikul Apilak Worachartcheewan Sakda Yainoy Natthakaln Lomchoey Paveena Kittiphatcharin Somsak Ruchirawat Virapong Prachayasittikul Center for Innovation Development and Technology Transfer Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand Center of Data Mining and Biomedical Informatics Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand Department of Chemistry Faculty of Science Srinakharinwirot University Bangkok 10110 Thailand Laboratory of Medicinal Chemistry Chulabhorn Research Institute and Chulabhorn Graduate Institute Bangkok 10210 Thailand Department of Clinical Microbiology and Applied Technology Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand

Objective: To investigate antioxidant and antimicrobial activities of Saraca thaipingensis Cantley ex Prain; and isolation of its flower extracts. Methods: The plant species (flowers, leaves, and twigs) were extracted by hexane, dichloromethane, ethyl acetate and methanol; and tested for antioxidant activity (DPPH assay) and antimicrobial activity (agar dilution method) against twenty-seven strains of microorganisms; gram positive and gram negative bacteria, and diploid fungus. Bioactive constituents were isolated by column chromatography. Results: The plant extracts has been firstly reported to display strong antioxidant activity and antimicrobial activity selective against gram positive bacteria (Corynebacterium diphtheriae NCTC 10356 and Streptococcus pyogenes) with MIC of 256 μg/mL. Stigmasterol and a mixture of triterpenoids and phenolic compounds were isolated from the flower extracts. Conclusions: The study revealed that the S. thaipingensis is a new source of natural antioxidants and antimicrobials with potential for medicinal uses.

关键词： Saraca thaipingensis Cantley ex Prain Antioxidants Antimicrobials Cytotoxicity Triterpenoids

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Erratum to: Methods for evaluating medical tests and biomarkers

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Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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Learning-based automatic breast tumor detection and segmentation in ultrasound images

Learning-based automatic breast tumor detection and segmenta...

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IEEE International Symposium on biomedical Imaging

作者： Peng Jiang Jingliang Peng Guoquan Zhang Erkang Cheng Vasileios Megalooikonomou Haibin Ling School of Computer Science and Technology Shandong Provincial Key Laboratory of Software Engineering P.R.China Department of Ultrasound Shandong Provincial Hospital Affiliated to Shandong University P.R. China Center for Data Analytics & Biomedical Informatics Temple University Philadelphia PA USA

ISBN: (纸本)9781457718571

Ultrasound (US) images have been widely used in the diagnosis of breast cancer in particular. While experienced doctors may locate the tumor regions in a US image manually, it is highly desirable to develop algorithms that automatically detect the tumor regions in order to assist medical diagnosis. In this paper, we propose a novel algorithm for automatic detection of breast tumors in US images. We formulate the tumor detection as a two step learning problem: tumor localization by bounding box and exact boundary delineation. Specifically, the proposed method uses an AdaBoost classifier on Harr-like features to detect a preliminary set of tumor regions. The preliminarily detected tumor regions are further screened with a support vector machine using quantized intensity features. Finally, the random walk segmentation algorithm is performed on the US image to retrieve the boundary of each detected tumor region. The proposed method has been evaluated on a data set containing 112 breast US images, including histologically confirmed 80 diseased ones and 32 normal ones. The data set contains one image from each patient and the patients are from 31 to 75 years old. Experiments demonstrate that the proposed algorithm can automatically detect breast tumors, with their locations and boundary shapes retrieved with high accuracy.

关键词： Image segmentation Support vector machines Breast tumors Ultrasonic imaging Feature extraction Accuracy

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Learning-based vessel segmentation in mammographic images

Learning-based vessel segmentation in mammographic images

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2011 1st IEEE International Conference on Healthcare informatics, Imaging and Systems Biology, HISB 2011

作者： Cheng, Erkang McLaughlin, Shawn Megalooikonomou, Vasileios Bakic, Predrag R. Maidment, Andrew D.A. Ling, Haibin Center for Data Analytics and Biomedical Informatics Computer and Information Science Department Temple University Philadelphia PA United States Temple University Philadelphia PA United States Department of Radiology University of Pennsylvania Philadelphia PA United States

ISBN: (纸本)9780769544076

In this paper we propose using a learning-based method for vessel segmentation in mammographic images. To capture the large variation in vessel patterns not only across subjects, but also within a subject, we create a feature pool containing local, Gabor and Haar features extracted from mammographic images generating a feature space of very high dimension. We also employ a huge number of training samples, which essentially contains the pixels in the training images. To deal with the very high dimensional feature space and the huge number of training samples, we apply a forest with boosting trees for vessel segmentation. Specifically, we use the standard AdaBoost algorithm for each tree in the forest. The randomness is encoded, when training each AdaBoost tree, using randomly sampled training set (pixels) and randomly selected features from the whole feature pool. The proposed method is tested using a real dataset with 20 anonymous mammographic images. The effectiveness of the proposed features and classifiers is demonstrated in the experiments where we compare different approaches and feature combinations. In the paper, we also present full analysis of different types of features. © 2011 IEEE.

关键词： Pixels

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Preservative License Plate De-identification for Privacy Protection

Preservative License Plate De-identification for Privacy Pro...

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International Conference on Document Analysis and Recognition

作者： Liang Du Haibin Ling Center for Data Analytics & Biomedical Informatics Department of Computer & Information Science Temple University Philadelphia PA USA

Advances in imaging devices and web technologies have brought dramatic improvements in collecting, storing, and sharing images. The leakage of privacy information in the process becomes an important issue that has started drawing attention from both academia and industry. In this work, we study the problem of privacy preserving with focus on license plate number protecting in imagery. Specifically, we present a novel method for de-identifying license plate images with the least degradation in image visual quality for privacy protection. Unlike previous de-identification methods that pay little attention to the image quality preservation, our method, named inhomogeneous principal component blur (IPCB), adaptively blurs different pixels of a license plate by taking into account the prior distribution of sensitive information. We tested the proposed method on a public dataset in comparison with several popular de-identification methods. The evaluation shows that our method successfully de-identified the privacy information with the least damage of image quality when compared with several other solutions.

关键词： Licenses Privacy Image quality Degradation data privacy Nonhomogeneous media Visualization

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Visual tracking based on log-Euclidean Riemannian sparse representation

Visual tracking based on log-Euclidean Riemannian sparse rep...

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7th International Symposium on Visual Computing, ISVC 2011

作者： Wu, Yi Ling, Haibin Blasch, Erik Bai, Li Chen, Genshe Center for Data Analytics and Biomedical Informatics Computer and Information Science Department Temple University Philadelphia PA United States Air Force Research Lab. SNAA OH United States Electrical and Computer Engineering Department Temple University Philadelphia PA United States

ISBN: (纸本)9783642240270

Recently, sparse representation has been utilized in many computer vision tasks and adapted for visual tracking. Sparsity-based visual tracking is formulated as searching candidates with minimal reconstruction errors from a template subspace with sparsity constraints in the approximation coefficients. However, an intensity template is easily corrupted by noise and not robust for target tracking under a dynamic environment. The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. Further, the covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized, and its dimension is small. Although the covariance matrix lies on Riemannian manifolds, its log-transformation can be measured on a Euclidean subspace. Based on the covariance region descriptor and using the sparse representation, we propose a novel tracking approach on the Log-Euclidean Riemannian subspace. Specifically, the target region is characterized by a covariance matrix which is then log-transformed from the Riemannian manifold to the Euclidean subspace. After that, the target tracking problem is integrated under a sparse approximation framework, where the sparsity is achieved by solving an 1- regularization problem. Then the candidate with the smallest approximation is taken as the tracked target. For target propagation, we use the Bayesian state inference framework, which propagates sample distributions over time using the particle filter algorithm. To evaluate our method, we have collected several video sequences and the experimental results show that our tracker can achieve robustly and reliably target tracking. © 2011 Springer-Verlag.

关键词： Target tracking

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Automatic Dent-landmark Detection in 3-D CBCT Dental Volumes

Automatic Dent-landmark Detection in 3-D CBCT Dental Volumes

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Erkang Cheng Jinwu Chen Jie Yang Huiyang Deng Yi Wu Vasileios Megalooikonomou Bryce Gable Haibin Ling Center for Data Analytics & Biomedical Informatics Computer & Information Science Department Temple University Division of Oral and Maxillofacial Radiology School of Dentistry Temple University College of Computer and Software Nanjing University of Information Science & Technology Data Engineering Laboratory Temple University

ISBN: (纸本)9781424441211

Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the key landmarks to construct the midsagittal reference plane. In this paper, we propose a learning-based approach to automatically detect the Dent-landmark in the 3D cone-beam computed tomography (CBCT) dental data. Specifically, a detector is learned using the random forest with sampled context features. Furthermore, we use spacial prior to build a constrained search space other than use the full three dimensional space. The proposed method has been evaluated on a dataset containing 73 CBCT dental volumes and yields promising results.

关键词： Three dimensional displays Training Context Feature extraction Vegetation Dentistry Testing

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Learning-Based Vessel Segmentation in Mammographic Images

Learning-Based Vessel Segmentation in Mammographic Images

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IEEE International Conference on Healthcare informatics, Imaging and Systems Biology (HISB)

作者： Erkang Cheng Shawn McLaughlin Vasileios Megalooikonomou Predrag R. Bakic Andrew D.A. Maidment Haibin Ling Center of Data Analytics & Biomedical Informatics Computer & Information Science Department Temple University Philadelphia PA USA Data Engineering Laboratory (DEnLab) Temple University Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA

关键词： Vegetation Training Feature extraction Boosting Image segmentation Probabilistic logic Testing

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The All of Us data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for biomedical Research

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biomedical data science 1000年第1期6卷 443-464页

作者： Kelsey R. Mayo Melissa A. Basford Robert J. Carroll Moira Dillon Heather Fullen Jesse Leung Hiral Master Shimon Rura Lina Sulieman Nan Kennedy Eric Banks David Bernick Asmita Gauchan Lee Lichtenstein Brandy M. Mapes Kayla Marginean Steve L. Nyemba Andrea Ramirez Charissa Rotundo Keri Wolfe Weiyi Xia Romuladus E. Azuine Robert M. Cronin Joshua C. Denny Abel Kho Christopher Lunt Bradley Malin Karthik Natarajan Consuelo H. Wilkins Hua Xu George Hripcsak Dan M. Roden Anthony A. Philippakis David Glazer Paul A. Harris 1Vanderbilt Institute for Clinical and Translational Research Vanderbilt University Medical Center Nashville Tennessee USA 2Deparment of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA email: paul.a.harris@*** 3Verily Life Sciences South San Francisco California USA 4Data Sciences Platform Broad Institute of Harvard and MIT Cambridge Massachusetts USA 5Broad Institute of Harvard and MIT Cambridge Massachusetts USA 6The All of Us Research Program National Institutes of Health Bethesda Maryland USA 7Vanderbilt University Medical Center Enterprise Cybersecurity Vanderbilt University Medical Center Nashville Tennessee USA 8Department of Internal Medicine The Ohio State University Columbus Ohio USA 9Department of Medicine and Institute for Augmented Intelligence in Medicine Feinberg School of Medicine Northwestern University Chicago Illinois USA 10Department of Biomedical Informatics Columbia University New York NY USA 11Department of Medicine Vanderbilt University Medical Center Nashville Tennessee USA 12Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA 13Department of Pharmacology Vanderbilt University Medical Center Nashville Tennessee USA

The All of Us Research Program's data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.

关键词： precision medicine data ecosystem diversity data privacy data integration electronic health records

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