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检索条件"机构=Research Center of Machine Learning and Data Analysis"
298 条 记 录,以下是211-220 订阅
排序:
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning
arXiv
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arXiv 2018年
作者: Trajanovski, Stojan Mavroeidis, Dimitrios Swisher, Christine Leon Gebre, Binyam Gebrekidan Veeling, Bastiaan S. Wiemker, Rafael Klinder, Tobias Tahmasebi, Amir Regis, Shawn M. Wald, Christoph McKee, Brady J. Flacke, Sebastian MacMahon, Heber Pien, Homer Data Science department Philips Research AE Eindhoven5656 Netherlands Human Longevity Inc. San DiegoCA92121 United States Research was done while C.L.S. was with Philips Research North America Cambridge MA02141 United States Machine Learning lab University of Amsterdam 1090 GH Amsterdam Philips Research AE Eindhoven5656 Netherlands Digital Imaging department Philips Research Hamburg22335 Germany Philips Research North America CambridgeMA02141 United States Lahey Hospital & Medical Center BurlingtonMA01805 United States Department of Radiology University of Chicago ChicagoIL60637 United States
Importance: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and it has been recently demonstrated that low-dose com... 详细信息
来源: 评论
E-service quality from attributes to outcomes: The similarity and difference between digital and hybrid services
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Journal of Open Innovation: Technology, Market, and Complexity 2020年 第4期6卷 1-21页
作者: Vatolkina, Natalia Gorbashko, Elena Kamynina, Nadezhda Fedotkina, Olga Department of Management Bauman Moscow State Technical University (National Research University) Moscow 105005 Russian Federation Department of Project and Quality Management Saint Petersburg State University of Economics St. Petersburg 191023 Russian Federation Department of Land Law and State Registration of Real Estate Moscow State University of Geodesy and Cartography Moscow 105064 Russian Federation Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow 125167 Russian Federation
Our research goal is to offer an e-service quality model based on experience and multidimensional quality and compare its applicability for e-services to find differences and similarities in consumer perceptions and b... 详细信息
来源: 评论
learning instrumental variables with structural and non-gaussianity assumptions
The Journal of Machine Learning Research
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The Journal of machine learning research 2017年 第1期18卷
作者: Ricardo Silva Shohei Shimizu Department of Statistical Science and Centre for Computational Statistics and Machine Learning University College London UK The Center for Data Science Education and Research Shiga University Shiga Japan and The Institute of Scientific and Industrial Research Osaka University Japan
learning a causal effect from observational data requires strong assumptions. One possible method is to use instrumental variables, which are typically justified by background knowledge. It is possible, under further ... 详细信息
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Retraction Note: Efficient user authentication protocol for distributed multimedia mobile cloud environment
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Journal of Ambient Intelligence and Humanized Computing 2024年 第1期15卷 275-275页
作者: Vivekanandan, Manojkumar Sastry, V. N. Srinivasulu Reddy, U. Center for Mobile Banking (CMB) Institute for Development and Research in Banking Technology (IDRBT) Hyderabad India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India
来源: 评论
Multi-Task learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
arXiv
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arXiv 2022年
作者: Behdin, Kayhan Loewinger, Gabriel Kishida, Kenneth T. Parmigiani, Giovanni Mazumder, Rahul MIT Operations Research Center CambridgeMA United States Machine Learning Team National Institute on Mental Health BethesdaMD United States Department of Physiology and Pharmacology Department of Neurosurgery Wake Forest School of Medicine Winston SalemNC United States Department of Biostatistics Harvard School of Public Health BostonMA United States Department of Data Science Dana Farber Cancer Institute BostonMA United States MIT Sloan Schools of Management CambridgeMA United States
We consider a problem in Multi-Task learning (MTL) where multiple linear models are jointly trained on a collection of datasets ("tasks"). A key novelty of our framework is that it allows the sparsity patter... 详细信息
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Feasibility of multiomics tumor profiling for guiding treatment of melanoma
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Nature medicine 2025年 2025 May 27页
作者: Nicola Miglino Nora C Toussaint Alexander Ring Ximena Bonilla Marina Tusup Benedict Gosztonyi Tarun Mehra Gabriele Gut Francis Jacob Stephane Chevrier Kjong-Van Lehmann Ruben Casanova Andrea Jacobs Sujana Sivapatham Laura Boos Parisa Rahimzadeh Manuel Schuerch Bettina Sobottka Natalia Chicherova Shuqing Yu Rebekka Wegmann Julien Mena Emanuela S Milani Sandra Goetze Cinzia Esposito Jacobo Sarabia Del Castillo Anja L Frei Marta Nowak Anja Irmisch Jack Kuipers Monica-Andreea Baciu-Drăgan Pedro F Ferreira Franziska Singer Anne Bertolini Michael Prummer Ulrike Lischetti Rudolf Aebersold Marina Bacac Gerd Maass Holger Moch Michael Weller Alexandre P A Theocharides Markus G Manz Niko Beerenwinkel Christian Beisel Lucas Pelkmans Berend Snijder Bernd Wollscheid Viola Heinzelmann Bernd Bodenmiller Mitchell P Levesque Viktor H Koelzer Gunnar Rätsch Reinhard Dummer Andreas Wicki Department of Medical Oncology and Hematology University of Zurich and University Hospital Zurich Switzerland. NEXUS Personalized Health Technologies ETH Zurich Zurich Switzerland. SIB Swiss Institute of Bioinformatics Lausanne Switzerland. Swiss Data Science Center SDSC Zurich Switzerland. Department of Computer Science Institute of Machine Learning ETH Zurich Zurich Switzerland. Department of Dermatology University Hospital Zurich University of Zurich Zurich Switzerland. Department of Biomedicine University Hospital Basel and University of Basel Basel Switzerland. Department of Quantitative Biomedicine University of Zurich Zurich Switzerland. Department of Biology RWTH Aachen Aachen Germany. Department of Pathology and Molecular Pathology University of Zurich and University Hospital Zurich Switzerland. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. Department of Health Sciences and Technology ETH Zurich Zurich Switzerland. ETH PHRT Swiss Multi-Omics Center (SMOC) ETH Zurich Zurich Switzerland. Department of Molecular Life Sciences University of Zurich Zurich Switzerland. Roche Pharmaceutical Research and Early Development Roche Innovation Center Zurich Switzerland. Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland. Roche Diagnostics GmbH MWG Penzberg Germany. Department of Neurology University Hospital and University of Zurich Zurich Switzerland. Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland. Biomedical Informatics University Hospital Zurich Zurich Switzerland. AI Center at ETH Zurich ETH Zurich Zurich Switzerland. Department of Biology ETH Zurich Zurich Switzerland. Department of Medical Oncology and Hematology University of Zurich and University Hospital Zurich Switzerland. andreas.wicki@usz.ch.
There is limited evidence supporting the feasibility of using omics and functional technologies to inform treatment decisions. Here we present results from a cohort of 116 melanoma patients in the prospective, multice...
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A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes
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Computers in Biology and Medicine 2024年 173卷 108306-108306页
作者: Kazemi, Azar Rasouli-Saravani, Ashkan Gharib, Masoumeh Albuquerque, Tomé Eslami, Saeid Schüffler, Peter J. Department of Medical Informatics School of Medicine Mashhad University of Medical Sciences Mashhad Iran Institute of General and Surgical Pathology Technical University of Munich Munich Germany Student Research Committee Department of Immunology School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran Department of Pathology Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran INESC TEC -Rua Dr. Roberto Frias Porto Portugal Pharmaceutical Sciences Research Center Institute of Pharmaceutical Technology Mashhad University of Medical Sciences Mashhad Iran Department of Medical Informatics University of Amsterdam Amsterdam Netherlands TUM School of Computation Information and Technology Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Munich Germany
The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on... 详细信息
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Ndmfcs: An Automatic Fruit Counting System in Modern Apple Orchard Using Abatement of Abnormal Fruit Detection
SSRN
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SSRN 2023年
作者: Wu, Zhenchao Sun, Xiaoming Jiang, Hanhui Mao, Wulan Li, Rui Andriyanov, Nikita Soloviev, Vladimir Fu, Longsheng College of Mechanical and Electronic Engineering Northwest A&F University Shaanxi Yangling712100 China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Shaanxi Yangling712100 China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Shaanxi Yangling712100 China Northwest A&F University Shenzhen Research Institute Guangdong Shenzhen518000 China Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow125167 Russia Institute of Agricultural Mechanization Xinjiang Academy of Agricultural Sciences Urumqi830000 China
Automatic fruit counting is an important task for growers to estimate yield and manage orchards. Although many deep-learning-based fruit detection algorithms have been developed to improve performance of automatic fru... 详细信息
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Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical research
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Digital Biomarkers 2019年 第3期3卷 116-132页
作者: Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom
Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clin... 详细信息
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S3Attention: Improving Long Sequence Attention with Smoothed Skeleton Sketching
arXiv
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arXiv 2024年
作者: Wang, Xue Zhou, Tian Zhu, Jianqing Liu, Jialin Yuan, Kun Yao, Tao Yin, Wotao Jin, Rong Cai, Han Qin Alibaba Group BellevueWA98004 United States Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Statistics and Data Science University of Central Florida OrlandoFL32816 United States Center for Machine Learning Research Peking University Beijing100871 China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200030 China Meta Menlo ParkCA94025 United States Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL32816 United States
Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models hard to apply to long sequence tasks... 详细信息
来源: 评论