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arXiv 2017年

作者： Cheplygina, Veronika van Opbroek, Annegreet Arfan Ikram, M. Vernooij, Meike W. de Bruijne, Marleen Biomedical Imaging Group Rotterdam Depts. Radiology and Medical Informatics Erasmus Medical Center Rotterdam Netherlands Pattern Recognition Laboratory Dept. Intelligent Systems Delft University of Technology Delft Netherlands Dept. Epidemiology and Radiology Erasmus Medical Center Rotterdam Netherlands The Image Section Dept. Computer Science University of Copenhagen Copenhagen Denmark

Supervised learning has been very successful for automatic segmentation of images from a single scanner. However, several papers report deteriorated performances when using classifiers trained on images from one scanner to segment images from other scanners. We propose a transfer learning classifier that adapts to differences between training and test images. This method uses a weighted ensemble of classifiers trained on individual images. The weight of each classifier is determined by the similarity between its training image and the test image. We examine three unsupervised similarity measures, which can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. The measures are based on a divergence, a bag distance, and on estimating the labels with a clustering procedure. These measures are asymmetric. We study whether the asymmetry can improve classification. Out of the three similarity measures, the bag similarity measure is the most robust across different studies and achieves excellent results on four brain tissue segmentation datasets and three white matter lesion segmentation datasets, acquired at different centers and with different scanners and scanning protocols. We show that the asymmetry can indeed be informative, and that computing the similarity from the test image to the training images is more appropriate than the opposite direction. Copyright © 2017, The Authors. All rights reserved.

关键词： Scanning

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Early experiences with crowdsourcing airway annotations in chest CT

arXiv

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arXiv 2017年

作者： Cheplygina, Veronika Perez-Rovira, Adria Kuo, Wieying Tiddens, Harm A.W.M. de Bruijne, Marleen Biomedical Imaging Group Rotterdam Depts Medical Informatics and Radiology Erasmus Medical Center Netherlands Pattern Recognition Laboratory Delft University of Technology Netherlands Dept of Pediatric Pulmonology and Allergology Erasmus Medical Center - Sophia Children's Hospital Rotterdam Netherlands Dept of Radiology Erasmus Medical Center Rotterdam Netherlands Image Section Dept of Computer Science University of Copenhagen Denmark

Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated data to perform well. We investigate whether crowdsourcing can be used to gather airway annotations which can serve directly for measuring the airways, or as training data for the algorithms. We generate image slices at known locations of airways and request untrained crowd workers to outline the airway lumen and airway wall. Our results show that the workers are able to interpret the images, but that the instructions are too complex, leading to many unusable annotations. After excluding unusable annotations, quantitative results show medium to high correlations with expert measurements of the airways. Based on this positive experience, we describe a number of further research directions and provide insight into the challenges of crowdsourcing in medical images from the perspective of first-time users. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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Active learning using uncertainty information

Active learning using uncertainty information

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International Conference on pattern recognition

作者： Yazhou Yang Marco Loog College of Information System and Management National University of Defense Technology Changsha China Pattern Recognition Laboratory Delft University of Technology Delft The Netherlands The Image Section University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9781509048489

Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classification model, and evaluate the criteria that we base our selection on. However, since the true label of the selected instance is unknown, these methods resort to calculating the average-case or worse-case performance with respect to the unknown label. In this paper, we propose a different method to solve this problem. In particular, our method aims to make use of the uncertainty information to enhance the performance of retraining-based models. We apply our method to two state-of-the-art algorithms and carry out extensive experiments on a wide variety of real-world datasets. The results clearly demonstrate the effectiveness of the proposed method and indicate it can reduce human labeling efforts in many real-life applications.

关键词： Linear programming Uncertainty Labeling Logistics Learning systems Measurement uncertainty pattern recognition

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IEEE International Symposium on Biomedical Imaging

作者： V Cheplygina A. van Opbroek M. A. Ikram M. W. Vernooij M. de Bruijne Biomedical Imaging Group Rotterdam Erasmus Medical Center The Netherlands Pattern Recognition Laboratory Delft University of Technology The Netherlands Dept. Epidemiology and Radiology Erasmus Medical Center The Netherlands The Image Section University of Copenhagen Denmark

ISBN: (纸本)9781479923519

Supervised classification is widely used for image segmentation. To work effectively, these techniques need large amounts of labeled training data, that is representative of the test data. Different patient groups, different scanners or different scanning protocols can lead to differences between the images, thus representative data might not be available. Transfer learning techniques can be used to account for these differences, thus taking advantage of all the available data acquired with different protocols. We investigate the use of classifier ensembles, where each classifier is weighted according to the similarity between the data it is trained on, and the data it needs to segment. We examine 3 asymmetric similarity measures that can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. We show that the asymmetry is informative and the direction of measurement needs to be chosen carefully. We also show that a point set similarity measure is robust across different studies, and outperforms state-of-the-art results on a multi-center brain tissue segmentation task.

关键词： Training Image segmentation Protocols Brain Biomedical imaging Biomedical measurement Training data

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OUGENE： a disease associated over-expressed and under-expressed gene database

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Science Bulletin 2016年第10期61卷 752-754页

作者： Xiaoyong Pan Hong-Bin Shen Section for Animal Genetics Bioinformatics and Breeding Center for Non-Coding RNA in Technology and Health University of Copenhagen 1870 Frederiksberg Denmark Department of Disease Systems Biology Novo NordiskFoundation Center for Protein Research University ofCopenhagen 1870 Frederiksberg Denmark Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Gene over-expression or under-expression is closely associated with human diseases, which contributes to phenotypic variations and diversity. To our best knowledge, there is no single open specific resource available to provide the association information between gene over- or under-expression and various diseases. In this study, we presented a comprehensive disease-associated over- and under-expressed gene database （OUGene） based on our proposed text mining pipeline and several open curated databases. It contains total 41,269 unique associa- tions between 7,238 over- or under-expressed genes and 1,480 diseases, which are supported by 81,974 evidence sentences from 56,442 articles. The OUGene is compre- hensive and covers most important therapeutic areas. Meanwhile a new scoring system is designed to rank the associations based on benchmarking against hand-curated data. OUGene provides an easy-of-use web interface for researchers to analyze these data and visualize the associ- ated networks, which can give insights to the complex relationships between over- and under-expressed genes and diseases at a system level. It is available at ***. ***/bioinf/OUGene/.

关键词： Text mining Gene over- and under-expression Disease Database

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Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

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Acta neuropathologica 2020年第5期139卷 959-962页

作者： Sven J van der Lee Olivia J Conway Iris Jansen Minerva M Carrasquillo Luca Kleineidam Erik van den Akker Isabel Hernández Kristel R van Eijk Najada Stringa Jason A Chen Anna Zettergren Till F M Andlauer Monica Diez-Fairen Javier Simon-Sanchez Alberto Lleó Henrik Zetterberg Marianne Nygaard Cornelis Blauwendraat Jeanne E Savage Jonas Mengel-From Sonia Moreno-Grau Michael Wagner Juan Fortea Michael J Keogh Kaj Blennow Ingmar Skoog Manuel A Friese Olga Pletnikova Miren Zulaica Carmen Lage Itziar de Rojas Steffi Riedel-Heller Ignacio Illán-Gala Wei Wei Bernard Jeune Adelina Orellana Florian Then Bergh Xue Wang Marc Hulsman Nina Beker Niccolo Tesi Christopher M Morris Begoña Indakoetxea Lyduine E Collij Martin Scherer Estrella Morenas-Rodríguez James W Ironside Bart N M van Berckel Daniel Alcolea Heinz Wiendl Samantha L Strickland Pau Pastor Eloy Rodríguez Rodríguez Bradley F Boeve Ronald C Petersen Tanis J Ferman Jay A van Gerpen Marcel J T Reinders Ryan J Uitti Lluís Tárraga Wolfgang Maier Oriol Dols-Icardo Amit Kawalia Maria Carolina Dalmasso Mercè Boada Uwe K Zettl Natasja M van Schoor Marian Beekman Mariet Allen Eliezer Masliah Adolfo López de Munain Alexander Pantelyat Zbigniew K Wszolek Owen A Ross Dennis W Dickson Neill R Graff-Radford David Knopman Rosa Rademakers Afina W Lemstra Yolande A L Pijnenburg Philip Scheltens Thomas Gasser Patrick F Chinnery Bernhard Hemmer Martijn A Huisman Juan Troncoso Fermin Moreno Ellen A Nohr Thorkild I A Sørensen Peter Heutink Pascual Sánchez-Juan Danielle Posthuma Jordi Clarimón Kaare Christensen Nilüfer Ertekin-Taner Sonja W Scholz Alfredo Ramirez Agustín Ruiz Eline Slagboom Wiesje M van der Flier Henne Holstege Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. s.j.vanderlee@amsterdamumc.nl. Department of Clinical Genetics Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. s.j.vanderlee@amsterdamumc.nl. Department of Neuroscience Mayo Clinic Florida Jacksonville FL 32224 USA. Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Department of Complex Trait Genetics Center for Neurogenomics and Cognitive Research Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Department for Neurodegenerative Diseases and Geriatric Psychiatry University of Bonn Bonn Germany. DZNE German Center for Neurodegenerative Diseases Bonn Germany. Division of Neurogenetics and Molecular Psychiatry Department of Psychiatry and Psychotherapy Faculty of Medicine University Hospital Cologne Cologne Germany. Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands. Pattern Recognition and Bioinformatics Delft University of Technology Delft The Netherlands. Research Center and Memory Clinic Fundació ACE Institut Català de Neurociències Aplicades Universitat Internacional de Catalunya Barcelona Spain. Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED) Madrid Spain. Department of Neurology Brain Center Rudolf Magnus University Medical Center Utrecht Utrecht The Netherlands. Amsterdam UMC-Vrije Universiteit Amsterdam Department of Epidemiology and Biostatistics Amsterdam Public Health Research Institute Amsterdam The Netherlands. Interdepartmental Program in Bioinformatics University of California Los Angeles USA. Neuropsychiatric Epidemiology Unit Department of Psychiatry and Neurochemistry Institute of Neuroscience and Physiology Sahlgrenska Academy Centre for Ageing and Health (AgeCap) at

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than o...

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Binary pattern flavored feature extractors for Facial Expression recognition: An overview

Binary pattern flavored feature extractors for Facial Expres...

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Proceedings of the International Convention MIPRO

作者： Rasmus Lyngby Kristensen Zheng-Hua Tan Zhanyu Ma Jun Guo Section of Image Analysis and Computer Graphics Technical University of Denmark Kgs. Lyngby Denmark Signal and Information Processing section (SIP) Aalborg University Aalborg Denmark Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781479981748

This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution is to provide a solid overview for future research.

关键词： Feature extraction Face Face recognition Databases Three-dimensional displays Accuracy Gold

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Graph-Based Kinship recognition

Graph-Based Kinship Recognition

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International Conference on pattern recognition

作者： Yuanhao Guo Hamdi Dibeklioglu Laurens Van Der Maaten Section Imaging & Bioinformatics Leiden University The Netherlands Pattern Recognition & Bioinformatics Group Delft University of Technology The Netherlands Pattern Recognition & Bioinf. Group Delft Univ. of Technol. Delft Zuid-Holland NL

ISBN: (纸本)9781479952106

Image-based kinship recognition is an important problem in the reconstruction and analysis of social networks. Prior studies on image-based kinship recognition have focused solely on pair wise kinship verification, i.e. on the question of whether or not two people are kin. Such approaches fail to exploit the fact that many real-world photographs contain several family members, for instance, the probability of two people being brothers increases when both people are recognized to have the same father. In this work, we propose a graph-based approach that incorporates facial similarities between all family members in a photograph in order to improve the performance of kinship recognition. In addition, we introduce a database of group photographs with kinship annotations.

关键词： Databases Feature extraction Face recognition Image recognition Accuracy Estimation Prediction algorithms

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Multi-modal pipeline for comprehensive validation of Mitral Valve geometry and functional computational models

Multi-modal pipeline for comprehensive validation of Mitral ...

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4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013

作者： Neumann, Dominik Grbic, Sasa Mansi, Tommaso Voigt, Ingmar Rabbah, Jean-Pierre Siefert, Andrew W. Saikrishnan, Neelakantan Yoganathan, Ajit P. Yuh, David D. Ionasec, Razvan Ioan Imaging and Computer Vision Siemens Corporate Technology Princeton NJ United States Pattern Recognition Lab. University of Erlangen-Nuremberg Germany Computer Aided Medical Procedures Technical University Munich Germany Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta GA United States Section of Cardiac Surgery Department of Surgery Yale University New Haven CT United States

ISBN: (纸本)9783642542671

Valvular heart disease affects a high number of patients, exhibiting significant mortality and morbidity rates. Mitral Valve (MV) Regurgitation, a disorder in which the MV does not close properly during systole, is among its most common forms. Traditionally, it has been treated with MV replacement. However, recently there is an increased interest in MV repair procedures, providing better long-term survival, better preservation of heart function, lower risk of complications, and usually eliminating the need for long-term use of blood thinners (anticoagulants). These procedures are complex and require an experienced surgeon and elaborate pre-operative planning. Hence, there is a need for efficient tools for training and planning of MV repair interventions. Computational models of valve function have been developed for these purposes. Nevertheless, state-of-the-art models remain approximations of real anatomy with considerable simplifications, since current modalities are limited by image quality. Hence, there is an important need to validate such low-fidelity models against comprehensive ex-vivo data to assess their clinical applicability. As a first step towards this aim, we propose an integrated pipeline for the validation of MV geometry and function models estimated in ex-vivo TEE data with respect to ex-vivo microCT data. We utilize a controlled experimental setup for ex-vivo imaging and employ robust machine learning and optimization techniques to extract reproducible geometrical models from both modalities. Using one exemplary case, we demonstrate the validity of our framework. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Pipelines

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