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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Retinal blood vessel segmentation using pareto ensemble prunning with diversity

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Journal of computers (Taiwan) 2019年第1期30卷 197-205页

作者： Liu, Pei-Zhong Liu, Ze-Fan Zhang, Yu-Zhao Zhang, Yong Feng, Hsuan-Ming College of Engineering Huaqiao University Quanzhou362021 China Quanzhou medical college Quanzhou362021 China Department of Computer Science and Information Engineer NationalQuemoy University No. 1 University Rd Kin-Ning Vallage Kinmen892 Taiwan Research Center for Applied Statistics and Big Data Huaqiao University No. 668 Jimei Avenue Xiamen361021 China

Ensemble learning way is effectively determined to form a group of base learners with the provided hypotheses combination, which improves performance of the traditional single classifier systems. How to improve the correct responses of base learners is one of the fundamental challenges in ensemble learning systems. Although Pareto Ensemble Pruning (PEP) can concurrently maximize the generalization performance and minimize the number of base learners in an ensemble system, it still has a question to be elucidated: diversity. Diversity is a necessary machine for high generalization capability in classifier ensemble. In this study, a novel Pareto Ensemble Pruning with Diversity (PEPD) approach is proposed on the basis of negative correlation learning and NSGA-II multi-objective genetic algorithm. There are three goals in PEPD: minimizing the error and size;maximizing the diversity. Experimental results on DRIVE data sets illustrate that the PEPD algorithm achieves the better performance than the other state-of-the-art approaches. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Learning systems

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Region-wise generative adversarial image inpainting for large missing areas

arXiv

引用

arXiv 2019年

作者： Ma, Yuqing Liu, Xianglong Bai, Shihao Wang, Lei Liu, Aishan Tao, Dacheng Hancock, Edwin State Key Lab of Software Development Environment Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia Department of Computer Science University of York York United Kingdom

Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and artifacts. Moreover, most inpainting approaches cannot well handle the large contiguous missing area cases. To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both contiguous and discontiguous large missing areas, in an adversarial manner. From which, region-wise convolution is deployed in both generator and discriminator to separately handle with the different regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guides the generator to obtain more information during inference. With the help of our proposed framework, we can restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, both on the large contiguous and discontiguous missing areas. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Approximation and competitive algorithms for single-minded selling problem 1

引用

12th International Conference on Algorithmic Aspects in Information and Management, AAIM 2018

作者： Chin, Francis Y. L. Poon, Sheung-Hung Ting, Hing-Fung Xu, Dachuan Yu, Dongxiao Zhang, Yong Department of Computer Science The University of Hong Kong Pok Fu Lam Hong Kong School of Computer Science University of Nottingham Ningbo China Ningbo China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing China Institute of Intelligent Computing School of Computer Science and Technology Shandong University Qingdao266237 China Research Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9783030046187

ISBN: (纸本)9783030046170

The problem of item selling with the objective of maximizing the revenue is studied. Given a seller with k types of items and n single-minded buyers, i.e., each buyer is only interested in a particular bundle of items, to maximize the revenue, the seller must carefully assign some amount of bundles to each buyer with respect to the buyer’s accepted price. Each buyer bi is associated with a value function vi(·) such that vi(x) is the accepted unit bundle price bi is willing to pay for x bundles. We show that the single-minded item selling problem is NP-hard. Moreover, we give an O(√k)-approximation algorithm. For the online version, i.e., the buyers come one by one and the decision on each buyer must be made before the arrival of the next buyer, an O(√k · (log h + log k))- competitive algorithm is achieved, where h is the highest unit item price among all buyers. © Springer Nature Switzerland AG 2018.

关键词： Sales

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A Comparative Study on Various Deep Learning Techniques for Thai NLP Lexical and Syntactic Tasks on Noisy data

A Comparative Study on Various Deep Learning Techniques for ...

引用

International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Amarin Jettakul Chavisa Thamjarat Kawin Liaowongphuthorn Can Udomcharoenchaikit Peerapon Vateekul Prachya Boonkwan Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Department of Computer Engineering Faculty of EngineeringChulalongkorn University Bangkok Thailand NECTEC Language and Semantic Technology Lab (LST) Pathumthani Thailand

In Natural Language Processing (NLP), there are three fundamental tasks of NLP which are Tokenization being a part of a lexical level, Part-of-Speech tagging (POS) and Named-Entity-Recognition (NER) being parts of a syntactic level. Recently, there have been many deep learning researches showing their success in many domains. However, there has been no comparative study for Thai NLP to suggest the most suitable technique for each task yet. In this paper, we aim to provide a performance comparison among various deep learning-based techniques on three NLP tasks, and study the effect on synthesized OOV words and the OOV handling algorithm with Levenshtein distance had been provided due to the fact that most existing works relied on a set of vocabularies in the trained model and not being fit for noisy text in the real use case. Our three experiments were conducted on BEST 2010 I2R, a standard Thai NLP corpus on F1 measurement, with the different percentage of noises having been synthesized. Firstly, for Tokenization, the result shows that Synthai, a jointed bidirectional LSTM, has the best performance. Additionally, for POS, bi-directional LSTM with CRF has obtained the best performance. For NER, variational bi-directional LSTM with CRF has outperformed other methods. Finally, the effect of noises reduces the performance of all algorithms on these foundation tasks and the result shows that our OOV handling technique could improve the performance on noisy data.

关键词： Task analysis Natural language processing Tagging Machine learning Noise measurement Training Memory management

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Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization

arXiv

引用

arXiv 2019年

作者： Yang, Yang Pesavento, Marius Luo, Zhi-Quan Ottersten, Björn Center for High Performance Computing Fraunhofer Institute for Industrial Mathematics Kaiserslautern67663 Germany Communication Systems Group Technische Universität Darmstadt Darmstadt64283 Germany Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States University of Luxembourg LuxembourgL-1855 Luxembourg

In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is selected and updated by solving the original optimization problem with respect to that block variable inexactly. More precisely, a local approximation of the original optimization problem is solved. The proposed algorithm has several attractive features, namely, i) high flexibility, as the approximation function only needs to be strictly convex and it does not have to be a global upper bound of the original function;ii) fast convergence, as the approximation function can be designed to exploit the problem structure at hand and the stepsize is calculated by the line search;iii) low complexity, as the approximation subproblems are much easier to solve and the line search scheme is carried out over a properly constructed differentiable function;iv) guaranteed (subsequence) convergence to a stationary point, even when the objective function does not have a Lipschitz continuous gradient. Interestingly, when the approximation subproblem is solved by a descent algorithm, (subsequence) convergence to a stationary point is still guaranteed even if the approximation subproblem is solved inexactly by terminating the descent algorithm after a finite number of iterations. These features make the proposed algorithm suitable for large-scale problems where the dimension exceeds the memory and/or the processing capability of the existing hardware. These features are also illustrated by several applications in signal processing and machine learning, for instance, network anomaly detection and phase retrieval. Copyright © 2019, The Authors. All rights reserved.

关键词： big data

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Spatio-temporal correlation analysis of online monitoring data for anomaly detection and location in distribution networks

arXiv

引用

arXiv 2018年

作者： Shi, Xin Qiu, Robert Ling, Zenan Yang, Fan Yang, Haosen He, Xing Department of Electrical Engineering Center for Big Data and Artificial Intelligence State Energy Smart Grid Research and Development Center Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505

The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal correlation analysis approach for anomaly detection and location in distribution networks. First, spatio-temporal matrix for each feeder line in a distribution network is formulated and the spectrum of its covariance matrix is analyzed. The spectrum is complex and exhibits two aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes, which represents factors caused by anomaly signals or fault disturbances. Then, by connecting the estimation of the number of factors to the limiting empirical spectral density of covariance matrices of residuals, the spatio-temporal parameters are accurately estimated, during which free random variable techniques are used. Based on the estimators, anomaly indicators are designed to detect and locate the anomalies by exploring the variations of spatio-temporal correlations in the data. The proposed approach is sensitive to the anomalies and robust to random fluctuations, which makes it possible for detecting early anomalies and reducing false alarming rate. Case studies on both synthetic data and real-world online monitoring data verify the effectiveness and advantages of the proposed approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Correlation methods

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An Accurate and Real-time Self-blast Glass Insulator Location Method Based on Faster R-CNN and U-net with Aerial Images

arXiv

引用

arXiv 2018年

作者： Ling, Zenan Qiu, Robert C. Jin, Zhijian Zhang, Yuhang He, Xing Liu, Haichun Lei, Chu Electrical and Computer Engineering Department Tennessee Technological University CookevilleTN38505 United States Department of Electrical Engineering Research Center for Big Data Engineering Technology State Energy Smart Grid Resarch and Development Center Shanghai Jiaotong University Shanghai200240 China

The location of broken insulators in aerial images is a challenging task. This paper, focusing on the self-blast glass insulator, proposes a deep learning solution. We address the broken insulators location problem as a low signal-noise-ratio image location framework with two modules: 1) object detection based on Fast R-CNN, and 2) classification of pixels based on U-net. A diverse aerial image set of some grid in China is tested to validated the proposed approach. Furthermore, a comparison is made among different methods and the result shows that our approach is accurate and real-time. Copyright © 2018, The Authors. All rights reserved.

关键词： Location

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A Novel Texture Generation Super Resolution Model

引用

Procedia computer Science 2019年 162卷 924-931页

作者： Biao Li Yong Shi Sujuan Li Bo Wang Zhiquan Qi Jiabin Liu School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA Department of Otology the First Affiliated Hospital of Zhengzhou University No. 1 eastern Jianshe road Zhengzhou Henan 450052 China School of Information Technology and Management University of International Business and Economics Beijing 100029 China Department of Computer Science and Engineering Texas A&M University TX 77843 USA

Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general, these new visual feeling promoting losses are constrained by the learning ability of model and effected by the weakness of GANs. In order to address these problems, we propose a novel SR method to generating abundant details. Based on former SR methods and texture generated models, we combine new model constructure and integrated loss function to achieve our goal. The new constructure derives from the very thought that more information from the LR input leads to more accurate results. The new integrated loss try to decrease the weakness of each one by corresponding loss.. Experiments show that our model achieves higher quality generations than former methods.

关键词： Super Resolution Multi Scale Smooth Loss Visual Quality

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