检索结果-内蒙古大学图书馆

A filter of minhash for image similarity measures

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Long, Jun Liu, Qunfeng Yuan, Xinpan Zhang, Chengyuan Liu, Junfeng School of Information Science and Engeering Central South University Big Data and Knowledge Engineering Institute Central South University School of Computer Hunan University of Technology

Image similarity measures play an important role in nearest neighbor search and duplicate detection for large-scale image datasets. Recently, Minwise Hashing (or Minhash) and its related hashing algorithms have achieved great performances in large-scale image retrieval systems. However, there are a large number of comparisons for image pairs in these applications, which may spend a lot of computation time and affect the performance. In order to quickly obtain the pairwise images that theirs similarities are higher than the specific threshold T (e.g., 0.5), we propose a dynamic threshold filter of Minwise Hashing for image similarity measures. It greatly reduces the calculation time by terminating the unnecessary comparisons in advance. We also find that the filter can be extended to other hashing algorithms, on when the estimator satisfies the binomial distribution, such as b-Bit Minwise Hashing, One Permutation Hashing, etc. In this pager, we use the Bag-of-Visual-Words (BoVW) model based on the Scale Invariant Feature Transform (SIFT) to represent the image features. We have proved that the filter is correct and effective through the experiment on real image datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Image analysis

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Chatterjee, Soumick Saad, Fatima Sarasaen, Chompunuch Ghosh, Suhita Krug, Valerie Khatun, Rupali Mishra, Rahul Desai, Nirja Radeva, Petia Rose, Georg Stober, Sebastian Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Artificial Intelligence Lab Otto von Guericke University Magdeburg Germany Department of Mathematics and Computer Science University of Barcelona Barcelona Spain Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Apollo Hospitals Bilaspur India HCG Cancer Centre Vadodara India Computer Vision Centre Cerdanyola Barcelona Spain Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected patients. Medical imaging, such as X-ray and Computed Tomography (CT), combined with the potential of Artificial Intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2 and DenseNet161) and their ensemble, using majority voting have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods - occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT, and using a global technique - neuron activation profiles. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

On the relationship between classical and computable topology 14

学校读者我要写书评

暂无评论

On the relationship between classical and computable topolog...

14th International Conference on Computability and Complexity in Analysis, CCA 2017

作者： Collins, Pieter Department of Data Science and Knowledge Engineering Maastricht University Bouillonstraat 8-10 Postbus 616 6200MD Netherlands

In this talk, we will look at some important concepts in classical topology, and discuss their computable counterparts. The main goals are to combine many important results which are scattered throughout the literature, and to -nd the most natural de-nitions for computable versions of classical properties. It turns out that many notions in computable topology coincide more closely with the corresponding notion in limit/sequential spaces than the classical topological notion. Further, for the class of spaces admitting an admissible representation, in many cases the de-nitions coincide;an important case are the various topologies on the open sets, including the Scott topology. © 2017 14th International Conference on Computability and Complexity in Analysis, CCA 2017 - Proceedings. All rights reserved.

关键词： Topology

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Ali, Sharib Dmitrieva, Mariia Ghatwary, Noha Bano, Sophia Polat, Gorkem Temizel, Alptekin Krenzer, Adrian Hekalo, Amar Guo, Yun Bo Matuszewski, Bogdan Gridach, Mourad Voiculescu, Irina Yoganand, Vishnusai Chavan, Arnav Raj, Aryan Nguyen, Nhan T. Tran, Dat Q. Huynh, Le Duy Boutry, Nicolas Rezvy, Shahadate Chen, Haijian Choi, Yoon Ho Subramanian, Anand Balasubramanian, Velmurugan Gao, Xiaohong W. Hu, Hongyu Liao, Yusheng Stoyanov, Danail Daul, Christian Realdon, Stefano Cannizzaro, Renato Lamarque, Dominique Tran-Nguyen, Terry Bailey, Adam Braden, Barbara East, James Rittscher, Jens Institute of Biomedical Engineering and Big Data Institute Old Road Campus University of Oxford Oxford United Kingdom Translational Gastroenterology Unit Experimental Medicine Div. John Radcliffe Hospital University of Oxford Oxford United Kingdom Oxford NIHR Biomedical Research Centre Oxford United Kingdom Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré France Instituto Onclologico Veneto IOV-IRCCS Padova Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Computer Engineering Department Arab Academy for Science and Technology Alexandria Egypt Department of Computer Science University College London London United Kingdom CRAN UMR 7039 University of Lorraine CNRS Nancy France Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Artificial Intelligence and Knowledge Systems University of Würzburg Germany School of Engineering University of Central Lancashire United Kingdom Department of Computer Science University of Oxford United Kingdom Mimyk Medical Simulations Pvt Ltd Indian Institute of Science Bengaluru India Dhanbad India Hanoi Viet Nam Le Kremlin-BicêtreF-94270 France School of Science and Technology Middlesex University London United Kingdom Department of Computer Science School of Informatics Xiamen University China Sungkyunkwan University Seoul Korea Republic of Claritrics India Pvt Ltd Chennai India School of Medical Science and Technology Indian Institute of Technology KharagpurWest Bengal India Shanghai Jiaotong University Shanghai China Ibn Zohr University Computer Science HIT Agadir Morocco

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques. © 2020, CC BY-NC-SA.

关键词： Endoscopy

iSplit LBI: Individualized partial ranking with ties via split LBI

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

Myocardial Ischemia Diagnosis Using a Reduced Lead System

学校读者我要写书评

暂无评论

Myocardial Ischemia Diagnosis Using a Reduced Lead System

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

作者： A. Aranda Hernandez P. Bonizzi J. Karel R. Peeters Department of Research and Technology Bakken Research Center Medtronic Maastricht Netherlands Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Universiteit Maastricht Maastricht Limburg NL

This research presents a novel statistical model for diagnosing acute myocardial infarction (AMI). The model is based on features extracted from a reduced lead system consisting of a subset of three leads from the standard 12-lead ECG. We selected a set of relevant parameters commonly used in the clinical practice for ECG-based AMI diagnosis, namely ST elevation and T-wave maximum. We also selected features, not used in clinical practice, that were derived from vectorcardiography and computed on the reduced three-lead system (pseudo-VCG parameters). To validate the model, we used 104 patients coming from the Physionet STAFF III database which contains 12-lead ECG recordings at baseline and in coronary artery occlusion condition during angioplasty (PTCA). Results show that pseudo-VCG features are able to diagnose AMI slightly better than ST elevation and T-wave maximum features together (area under the ROC curve (AUC) 0.87 vs AUC 0.85). When combining pseudo-VCG features together with ST elevation, and T-wave maximum, the performance improved significantly (AUC 0.95, sensitivity 89.6% and specificity 82.7%). Results indicate a potential for diagnosing AMI using the proposed reduced lead system and the selected set of features. We suggest its possible use for diagnosing AMI in long-term, ambulatory and home monitoring situations, allowing an earlier and faster diagnosis.

关键词： Electrocardiography Feature extraction Heart Myocardium databases Lead Standards

Corrigendum to: Multi-Swarm Cuckoo Search Algorithm withQ-Learning Model

学校读者我要写书评

暂无评论

The Computer Journal 2022年第7期65卷 1938-1938页

作者： Li, Juan Xiao, Dan-dan Zhang, Ting Liu, Chun Li, Yuan-xiang Wang, Gai-ge School of Artificial Intelligence Wuhan Technology and Business University Wuhan 430065 China School of Artificial Intelligence Wuchang University of Technology Wuhan 430223 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Street Gaoxin District Changchun Jilin China 130012 School of Computer Wuhan University No. 299 Bayi Street Wuchang District Wuhan Hubei China 430072 Department of Computer Science and Technology Ocean University of China No. 238 Songling Street Laoshan District Qingdao Shandong China 266100 Institute of Algorithm and Big Data Analysis Northeast Normal University No. 5268 Renmin Street Nanguan District Changchun Jilin China 130117

Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Bornmann, Lutz Tekles, Alexander Zhang, Helena H. Ye, Fred Y. Division for Science and Innovation Studies Administrative Headquarters of the Max Planck Society Hofgartenstr. 8 Munich80539 Germany Ludwig-Maximilians-Universität Munich Department of Sociology Konradstr. 6 Munich80801 Germany Jiangsu Key Laboratory of Data Engineering Knowledge Service School of Information Management Nanjing University Nanjing210023 China Nanjing University Nanjing China University of Illinois Champaign United States

Lee, Walsh, and Wang (2015) – based on Uzzi, Mukherjee, Stringer, and Jones (2013) – and Wang, Veugelers, and Stephan (2017) proposed scores based on cited references (cited journals) data which can be used to measure the novelty of papers (named as novelty scores U and W in this study). Although previous research has used novelty scores in various empirical analyses, no study has been published up to now – to the best of our knowledge – which quantitatively tested the convergent validity of novelty scores: do these scores measure what they propose to measure? Using novelty assessments by faculty members (FMs) at F1000Prime for comparison, we tested the convergent validity of the two novelty scores (U and W). FMs’ assessments not only refer to the quality of biomedical papers, but also to their characteristics (by assigning certain tags to the papers): for example, are the presented findings or formulated hypotheses novel (tags "new findings" and "hypothesis")? We used these and other tags to investigate the convergent validity of both novelty scores. Our study reveals different results for the novelty scores: the results for novelty score U are mostly in agreement with previously formulated expectations. We found, for instance, that for a standard deviation (one unit) increase in novelty score U, the expected number of assignments of the "new finding" tag increase by 7.47%. The results for novelty score W, however, do not reflect convergent validity with the FMs’ assessments: only the results for some tags are in agreement with the expectations. Thus, we propose – based on our results – the use of novelty score U for measuring novelty quantitatively, but question the use of novelty score W. Copyright © 2019, The Authors. All rights reserved.

关键词：

Author Correction:data augmentation in microscopic images for material data mining

学校读者我要写书评

暂无评论

npj Computational Materials 2020年第1期6卷 476-476页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China School of Materials Science and Engineering University of Science and Technology Beijing Beijing China School of Materials Science and Technology Liaoning Technical University Liaoning China The Institute of Statistical Mathematics Research Organization of Information and Systems Tachikawa Tokyo Japan National Intellectual Property Administration Beijing China Faculty of Engineering Sciences University of Burundi Bujumbura Burundi Faculty of Engineering and Technology Palestine Technical University – Kadoorie Tulkarem Palestine College of Information Science and Engineering China University of Petroleum Beijing China Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

The original version of this article omitted the following from the Acknowledgements:“This work was supported by Beijing Top Discipline for Artificial Intelligent science and engineering,University of science and Tec... 详细信息

关键词： HTML knowledge mining