261 results found
Yang G, Yu S, Dong H, et al., 2018, DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 37, Pages: 1310-1321, ISSN: 0278-0062
Takahashi K, Pavlidis S, Kwong FNK, et al., 2018, Sputum proteomics and airway cell transcripts of current and ex-smokers with severe asthma in U-BIOPRED: an exploratory analysis, EUROPEAN RESPIRATORY JOURNAL, Vol: 51, ISSN: 0903-1936
Hekking P-P, Loza MJ, Pavlidis S, et al., 2018, Pathway discovery using transcriptomic profiles in adult-onset severe asthma, JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, Vol: 141, Pages: 1280-1290, ISSN: 0091-6749
Dong H, Wu C, Wei Z, et al., 2018, Dropping Activation Outputs With Localized First-Layer Deep Network for Enhancing User Privacy and Data Security, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 13, Pages: 662-670, ISSN: 1556-6013
Bai L, Liang J, Du H, et al., 2018, A novel community detection algorithm based on simplification of complex networks, KNOWLEDGE-BASED SYSTEMS, Vol: 143, Pages: 58-64, ISSN: 0950-7051
Kuo C-HS, Liu C-Y, Pavlidis S, et al., 2018, Unique immune gene expression Patterns in Bronchoalveolar lavage and Tumor adjacent non-neoplastic lung Tissue in non-small cell lung cancer, FRONTIERS IN IMMUNOLOGY, Vol: 9, ISSN: 1664-3224
Wang Z, Xiao D, Fang F, et al., 2018, Model identification of reduced order fluid dynamics systems using deep learning, INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Vol: 86, Pages: 255-268, ISSN: 0271-2091
Li K, Liu F, Dong H, et al., 2018, A DEEP LEARNING PLATFORM FOR DIABETES BIG DATA ANALYSIS, Publisher: MARY ANN LIEBERT, INC, Pages: A116-A116, ISSN: 1520-9156
Dong H, Supratak A, Pan W, et al., 2018, Mixed Neural Network Approach for Temporal Sleep Stage Classification, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 26, Pages: 324-333, ISSN: 1534-4320
Rossios C, Pavlidis S, Hoda U, et al., 2018, Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma, JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, Vol: 141, Pages: 560-570, ISSN: 0091-6749
Liao B, Zhang J, Wu C, et al., 2018, Deep Sequence Learning with Auxiliary Information for Traffic Prediction, Publisher: ASSOC COMPUTING MACHINERY
Yang X, Pan W, Guo Y, 2017, Sparse Bayesian classification and feature selection for biological expression data with high correlations, PLOS ONE, Vol: 12, ISSN: 1932-6203
Ezzati M, Bentham J, Di Cesare M, et al., 2017, Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults, LANCET, Vol: 390, Pages: 2627-2642, ISSN: 0140-6736
Bai L, Cheng X, Liang J, et al., 2017, Fast density clustering strategies based on the k-means algorithm, PATTERN RECOGNITION, Vol: 71, Pages: 375-386, ISSN: 0031-3203
Supratak A, Dong H, Wu C, et al., 2017, DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 25, Pages: 1998-2008, ISSN: 1534-4320
Dong H, Supratak A, Mai L, et al., 2017, TensorLayer: A versatile library for efficient deep learning development, Pages: 1201-1204
© 2017 Copyright held by the owner/author(s). Recently we have observed emerging uses of deep learning techniques in multimedia systems. Developing a practical deep learning system is arduous and complex. It involves labor-intensive tasks for constructing sophisticated neural networks, coordinating multiple network models, and managing a large amount of training-related data. To facilitate such a development process, we propose TensorLayer which is a Python-based versatile deep learning library. TensorLayer provides high-level modules that abstract sophisticated operations towards neuron layers, network models, training data and dependent training jobs. In spite of offering simplicity, it has transparent module interfaces that allows developers to flexibly embed low-level controls within a backend engine, with the aim of supporting fine-grain tuning towards training. Real-world cluster experiment results show that TensorLayer is able to achieve competitive performance and scalability in critical deep learning tasks. TensorLayer was released in September 2016 on GitHub. Since after, it soon become one of the most popular open-sourced deep learning library used by researchers and practitioners.
Nie L, Yang X, Matthews PM, et al., 2017, Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation, INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING, Vol: 14, Pages: 371-385, ISSN: 1476-8186
Lefaudeux D, De Meulder B, Loza MJ, et al., 2017, U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics, JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, Vol: 139, Pages: 1797-1807, ISSN: 0091-6749
Bai L, Cheng X, Liang J, et al., 2017, Fast graph clustering with a new description model for community detection, INFORMATION SCIENCES, Vol: 388, Pages: 37-47, ISSN: 0020-0255
Molina-Solana M, Birch D, Guo Y-K, 2017, Improving data exploration in graphs with fuzzy logic and large-scale visualisation, APPLIED SOFT COMPUTING, Vol: 53, Pages: 227-235, ISSN: 1568-4946
He S, Yong M, Matthews PM, et al., 2017, tranSMART-XNAT Connector tranSMART-XNAT connector-image selection based on clinical phenotypes and genetic profiles, BIOINFORMATICS, Vol: 33, Pages: 787-788, ISSN: 1367-4803
Kuo C-HS, Pavlidis S, Loza M, et al., 2017, A Transcriptome-driven Analysis of Epithelial Brushings and Bronchial Biopsies to Define Asthma Phenotypes in U-BIOPRED, AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, Vol: 195, Pages: 443-455, ISSN: 1073-449X
Kuo C-HS, Pavlidis S, Loza M, et al., 2017, T-helper cell type 2 (Th2) and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in U-BIOPRED, EUROPEAN RESPIRATORY JOURNAL, Vol: 49, ISSN: 0903-1936
Pavlidis S, Guo Y, Sun K, et al., 2017, Comparison Between Bronchial And Nasal Brushings Gene Expression In The U-Biopred Cohort, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X
Oehmichen A, Guitton F, Sun K, et al., 2017, eTRIKS Analytical Environment: A Modular High Performance Framework for Medical Data Analysis, IEEE International Conference on Big Data (IEEE Big Data), Publisher: IEEE, Pages: 353-360, ISSN: 2639-1589
Dong H, Yang G, Liu F, et al., 2017, Automatic brain tumor detection and segmentation using U-net based fully convolutional networks, Pages: 506-517, ISSN: 1865-0929
© Springer International Publishing AG 2017. A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.
Du Y, Ma C, Wu C, et al., 2017, A Visual Analytics Approach for Station-Based Air Quality Data, SENSORS, Vol: 17, ISSN: 1424-8220
Lertvittayakumjorn P, Wu C, Liu Y, et al., 2017, Exploratory analysis of big social data using MIC/MINE statistics, Pages: 513-526, ISSN: 0302-9743
© 2017, Springer International Publishing AG. A major goal of Exploratory Data Analysis (EDA) is to understand main characteristics of a dataset, especially relationships between variables, which are helpful for creating a predictive model and analysing causality in social science research. This paper aims to introduce Maximal Information Coefficient (MIC) and its by-product statistics to social science researchers as effective EDA tools for big social data. A case study was conducted using a historical data of more than 3,000 country-level indicators. As a result, MIC and some by-product statistics successfully provided useful information for EDA complementing the traditional Pearson’s correlation. Moreover, they revealed several significant, including nonlinear, relationships between variables which are intriguing and able to suggest further research in social sciences.
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