检索结果-内蒙古大学图书馆

Methods for Evaluating Medical Tests and Biomarkers

Diagnostic and Prognostic Research 2017年第1期1卷 1-34页

作者： Gowri Gopalakrishna Miranda Langendam Patrick Bossuyt Mariska Leeflang Rob Scholten Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Marie Westwood Jos Kleinen Richard Riley Gary Collins Hans Reitsma Karel Moons Antonia Zapf Katharina Kramer Annika Hoyer Oliver Kuss J. Ensor R. D. Riley J. J. Deeks E. C. Martin Gerta Rücker Martin Schumacher Susanne Steinhauser Joie Ensor Kym Snell Brian Willis Jon Deeks Thomas Debray 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 Sabine Van Huffel Jan Verbakel 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 Peter Selby Doug Altman William Rosenberg Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Johannes Reitsma Anne Rutjes Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Bavo De Cock Daan Nieboer Ewout W. Steyerberg Micael J. Pencina 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 Zoe Plummer Barnaby Reeves Chris Rogers Andrew Mumford Kurtis Lee Veerle Verheyden Gianni D. Angelini Gavin J. Murphy Jeremy Huddy Melody Ni George Hanna Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands Department of Clinical Epidemiology and Biostatistics Academic Medical Center University of Amsterdam Amsterdam Netherlands Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK EPPI-Centre Department of Social Science University College London London UK Division of Health and Social Care Research King’s College London London UK College of Computer and Information Science Northeastern University Boston USA University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK Institute of Applied Health Research University of Birmingham Birmingham UK School of Social and Community Medicine University of Bristol Bristol UK Kleijnen Systematic Reviews Ltd York UK Research Institute for Primary Care and Health Sciences Keele University Keele UK Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Cochrane Netherlands University Medical Center Utrecht Utrecht The Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Institute for Biometry and Epidemiology German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf Germany Manchester Pharmacy School University of Manchester Manchester UK Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center – University of Freiburg Stefan-Meier-Str. 26 79104 Freiburg Germany

来源：评论

学校读者我要写书评

暂无评论

British Society of Breast Radiology Annual Scientific Meeting 2016

引用

Breast Cancer Research 2016年第1期18卷 1-20页

作者： Ameerah Mohd Azmil Matthew Wallis Gaurav Jyoti Bansal Eleri Davies Susan Astley Fleur Kilburn-Toppin Mohamad Hajaj Sian Taylor-Phillip Anne Buckley Nuala Healy Aine Quinn Sylvia O’Keeffe Teri Ang Anthony Maxwell Yit Y. Lim Elaine Harkness Soujanya Gadde Richard Emsley Guy Stevens Kate Gower Thomas Jennifer Ferguson Robert Hills Sabrina Rajan Nisha Sharma Ajit Kishore John O’ Dowd Claire Murphy Ken Lindsay Guneesh Dadayal Anne Nielsen Moody Madeleine Whitaker Stephen Wolstenhulme Jane Bull Anne-Marie Culpan Natalia Roszkowski Alexis Grima Michaela Stahnke Caroline Rubin Ruth Walker Rachel Oeppen Srikanth Puttagunta Karen Gray Shama Puri Anne Turnbull Mark Bagnall Joanne York Stuart Benney Amit Goyal Rebecca Millican-Slater Sana Pascaline Janet Flinn Kim Thomson Beatrix Elsberger Ashley Topps Simon Barr Panagiotis Pikoulas Susan Pritchard Muthyala Sreenivas Sophie Greenhalgh Anju Nandhra Sheetal Sharma Archita Gulati Furhan Razzaq Sana Khan Jacqueline McKillen Rupert Larkin Sachin Kamat Carla Goncalves Alan Tan Asha Eleti Nithya Vidyaprakash Michelle A. McMahon Barbara J. G. Dall Faisal Majid Ramachadra Rattehalli Vandana Gaur Alistair Mackenzie Lucy Warren Mishal Patel Mark Halling-Brown Kenneth Young Louise Wilkinson James Tanner Rachael Currie Sharron Balyes Julie Wragg Georgiana Zamfir Pallavi Ayra Max Marsden James Butterworth Shona Sinha Anil Desai Anna Metafa Anne Nielsen-Moody Laura Ward Samantha Heller Sue Hudson Kathryn Taylor Elizabeth Orlowski Argyro Xyda Steven Allen Kate Downey Richard Sidebottom Ahmad Lodhi Anthony Howell Gareth Evans Yit Lim Yan Chen Elizabeth Lepine-Williams Steven Henderson Janet Litherland Naveed Altaf Yvonne Bury Nerys Forester Mei Chan Chin Caroline Taylor Pippa Skippage Aneet Sian Rita McAvinchey Tom Evans Sian Nisbet Alex Murray Raquel Clark Castillo Hannah Markham Timothy Thomas Anil Jain Joseph Wan Jaanilka Molderson Helen Newman Simon Lowes Linsley Lunt Sadia Sultana Varun Chillal Rebecca Dalton David Reeves Jamie Sergeant Liz Edwards Kate Jenkins Shaheeda Shaikh Elizabeth Cardiff University Cardiff UK Cambridge Breast Unit Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK The Breast Centre University Hospital of Llandough Cardiff UK Nightingale Centre and Genesis Prevention Centre University Hospital of South Manchester Manchester UK Centre for Imaging Sciences Institute of Population Health University of Manchester Manchester UK University of Manchester Manchester UK The Nightingale Centre University Hospital of South Manchester Manchester UK Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester Manchester UK University of Warwick Coventry UK Department of Diagnostic Radiology St. James’s Hospital Dublin Ireland Manchester Medical School University of Manchester Manchester UK University Hospital of South Manchester NHS Foundation Trust Manchester UK Centre for Biostatistics Institute of Population Health University of Manchester Manchester UK Breast Test Wales Cardiff UK Public Health Wales Wales UK Cardiff University School of Medicine Cardiff UK Leeds Teaching Hospitals NHS Trust Leeds UK Leeds teaching Hospitals NHS Trust Leeds UK Leeds Teaching Hospitals Leeds UK The Leeds Teaching Hospitals NHS Trust Leeds UK St James’s University Hospital Leeds UK Leeds Teaching Hospitals Trust Leeds UK Airedale General Hospital Keighley/West Yorkshire UK University of Leeds Leeds UK University Hospital Southampton Southampton UK Hairmyres Hospital Lanarkshire Scotland UK Royal Derby Hospital Derby UK Kettering General Hospital Kettering UK Ninewells Hospital Dundee UK University Hospitals of Coventry and Warwickshire Coventry West Midlands UK University Hospitals Coventry and Warwickshire NHS Trust Coventry UK UHCW NHS Trust Coventry West Midlands UK University Hospital Coventry and Warwickshire Coventry UK Warwick Medical School Coventry West Midlands UK Queen Elizabeth Hospital Gateshead Gateshead UK Royal Liverpool University

来源：评论

学校读者我要写书评

暂无评论

Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes

Towards understanding dominant processes in complex dynamica...

引用

6th International Workshop on Knowledge Discovery from Sensor data, SensorKDD'12 - Held in Conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and data Mining, KDD 2012

作者： Das, Debasish Ganguly, Auroop Banerjee, Arindam Obradovic, Zoran Department of Civil and Environmental Engineering Northeastern University Boston MA United States Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA United States Computer Science and Engineering University of Minnesota Minneapolis MN United States

ISBN: (纸本)9781450315548

Complex dynamical systems like precipitation extremes under climate variability or change are typically governed by multiple processes at multiple scales. The processes themselves may be manifested at multiple scales and would need to be captured through key indicator variables, which in turn may be better projected by physical models than the variables of interest. We posit that hybrid approaches based on physically-motivated approaches and data-driven methods, which in turn are conditioned on both observations and simulations from large-scale physics-based models, may offer novel and quantifiable insights. The data-driven approaches may need to extend and adapt methods developed and tested in statistics, data mining and machine learning to the concept of dominant processes. In this paper, we performed some exploratory data analysis to characterize the effect of dominant processes on precipitation extremes, annually and seasonally, and from global and century scale to regional and decadal scale, and found some interesting insights that pointed towards need of improved understanding. We identified the gaps in understanding the regional drivers where data-driven methods can make useful improvements and eventually lead to a predictive model for precipitation extremes. Although we do not propose any specific method for solving the problem, we realize that any successful data mining solution should include all or a subset of tools like dimensionality reduction, grouped variable selection, non-linear regression and graphical models. The concepts of dominant processes proposed here would likely generalize broadly to climate extremes while the solution frameworks themselves may generalize beyond climate. Copyright 2012 ACM.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Early classification of multivariate time series using a hybrid HMM/SVM model

Early classification of multivariate time series using a hyb...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mohamed F. Ghalwash Dušan Ramljak Zoran Obradović Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA Mathematics Department Faculty of Science Ain Shams University Cairo Egypt

Early classification of time series has been receiving a lot of attention as of late, particularly in the context of gene expression. In the biomédical realm, early classification can be of tremendous help, by identifying the onset of a disease before it has time to fully take hold, or determining that a treatment has done its job and can be discontinued. In this paper we present a state-of-the-art model, which we call the Early Classification Model (ECM), that allows for early, accurate, and patient-specific classification of multivariate time series. The model is comprised of an integration of the widely-used HMM and SVM models, which, while not a new technique per se, has not been used for early classification of multivariate time series classification until now. It attained very promising results on the datasets we tested it on: in our experiments based on a published dataset of response to drug therapy in Multiple Sclerosis patients, ECM used only an average of 40% of a time series and was able to outperform some of the baseline models, which needed the full time series for classification.

关键词： Hidden Markov models Time series analysis Gene expression Support vector machines Accuracy Training data models

来源：评论

学校读者我要写书评

暂无评论

Online robust image alignment via iterative convex optimization

Online robust image alignment via iterative convex optimizat...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Wu Bin Shen Haibin Ling Center for Data Analytics & Biomedical Informatics Computer & Information Science Department Temple University Philadelphia PA USA Department of Computer Science Purdue University West Lafayette IN USA

In this paper we study the problem of online aligning a newly arrived image to previously well-aligned images. Inspired by recent advances in batch image alignment using low rank decomposition [16], we treat the newly arrived image, after alignment, as being linearly and sparsely reconstructed by the well-aligned ones. The task is accomplished by a sequence of convex optimization that minimizes the l\-norm. After that, online basis updating is pursued in two different ways: (1) a two-stage incremental alignment for joint registration of a large image dataset which is known a prior, and (2) a greedy online alignment of dynamically increasing image sequences, such as in the tracking scenario. In (1), we first sequentially collect basis images that are easily aligned by checking their reconstruction residuals, followed by the second stage where all images are re-aligned one-by-one using the collected basis set. In (2), during the tracking process, we dynamically enrich the image basis set by the new target if it significantly distinguishes itself from existing basis images. While inheriting the benefits of sparsity, our method enjoys the great time efficiency and therefore be capable of dealing with large image set and real time tasks such as visual tracking. The efficacy of the proposed online robust alignment algorithm is verified with extensive experiments on image set alignment and visual tracking, in reference with state-of-the-art methods.

关键词： Target tracking Visualization Face Robustness Joints Image reconstruction Convex functions

来源：评论

学校读者我要写书评

暂无评论

SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RARE EVENTS: A STUDY IN PRECIPITATION EXTREMES

SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RA...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Antioxidant and antimicrobial activities of Saraca thaipingensis Cantley ex Prain

引用

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

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Methods for evaluating medical tests and biomarkers

引用

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.].

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Learning-based vessel segmentation in mammographic images

Learning-based vessel segmentation in mammographic images

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：