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Dictionary learning

Ming Shi, Weiming Shen, Yanwen Chong, Hong-Qiang Wang
Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators...
December 2017: IET Systems Biology
Marco Basaldella, Lenz Furrer, Carlo Tasso, Fabio Rinaldi
BACKGROUND: This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. METHOD: The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only...
November 9, 2017: Journal of Biomedical Semantics
Chin Lin, Chia-Jung Hsu, Yu-Sheng Lou, Shih-Jen Yeh, Chia-Cheng Lee, Sui-Lung Su, Hsiang-Cheng Chen
BACKGROUND: Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN). OBJECTIVE: Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes...
November 6, 2017: Journal of Medical Internet Research
Pramod Kumar Pisharady, Stamatios N Sotiropoulos, Guillermo Sapiro, Christophe Lenglet
We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Xinzheng Zhang, Yijian Wang, Zhiying Tan, Dong Li, Shujun Liu, Tao Wang, Yongming Li
In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a ℓ 2 , 1 -norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary is constructed based on the training subset. The second stage of the method is to perform target images classification based on the new dictionary, utilizing multi-task collaborative representation...
November 1, 2017: Sensors
Jing Yuan, Xiang Li, Jinhe Zhang, Liao Luo, Qinglin Dong, Jinglei Lv, Yu Zhao, Xi Jiang, Shu Zhang, Wei Zhang, Tianming Liu
Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions...
November 9, 2017: NeuroImage
Fariba Shaker, S Amirhassan Monadjemi, Javad Alirezaie, Ahmad Reza Naghsh-Nilchi
BACKGROUND AND OBJECTIVE: To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes...
October 10, 2017: Computers in Biology and Medicine
O Bieliaieva, Yu Lysanets, K Havrylieva, I Znamenska, I Rozhenko, N Nikolaieva
The present paper examines the phenomenon of paronymy in the sublanguage of medicine. The study of paronyms plays an important role in the development of terminological competence of future specialists in the field of medicine and healthcare. The authors emphasize the need to pay due attention to terminological paronyms when compiling teaching manuals and developing didactic materials in Latin for students of medical universities. The urgency of organizing the work with these lexical units is determined, on the one hand, by the propaedeutic objective - minimization of difficulties that students may encounter in dealing with special terminology in the process of educational and professional communication; on the other hand, the study of paronyms is aimed at expanding the active and passive vocabulary of medical students...
October 2017: Georgian Medical News
Qianxiao Li, Felix Dietrich, Erik M Bollt, Ioannis G Kevrekidis
Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD)(51) and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism...
October 2017: Chaos
Jon Neville, Steve Kopko, Klaus Romero, Brian Corrigan, Bob Stafford, Elizabeth LeRoy, Steve Broadbent, Martin Cisneroz, Ethan Wilson, Eric Reiman, Hugo Vanderstichele, Stephen P Arnerić, Diane Stephenson
INTRODUCTION: The exceedingly high rate of failed trials in Alzheimer's disease (AD) calls for immediate attention to improve efficiencies and learning from past, ongoing, and future trials. Accurate, highly rigorous standardized data are at the core of meaningful scientific research. Data standards allow for proper integration of clinical data sets and represent the essential foundation for regulatory endorsement of drug development tools. Such tools increase the potential for success and accuracy of trial results...
June 2017: Alzheimer's & Dementia: Translational Research & Clinical Interventions
Nasrin Tavakoli, Maryam Karimi, Mansour Nejati, Nader Karimi, S M Reza Soroushmehr, Shadrokh Samavi, Kayvan Najarian
Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Rituparna Sarkar, Scott T Acton
In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features...
October 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jo Schlemper, Jose Caballero, Joseph V Hajnal, Anthony Price, Daniel Rueckert
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data is acquired using aggressive Cartesian undersampling. Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed...
October 13, 2017: IEEE Transactions on Medical Imaging
Ann-Britt Zakrisson
Patients with Chronic Obstructive Pulmonary Disease (COPD) have multiple symptoms. Nursing care is based on six core competencies and one of them is person-centred care that includes the aspect of professional symptom relief. The aim was to clarify a meaning of the concept of Symptom-reducing actions in the context of COPD. Databases MEDLINE and CINAHL were searched between 1982 and February 2016 and 26 publications were found. Two dictionaries and three books were investigated. The method of Walker & Avant was followed...
2017: International Journal of Qualitative Studies on Health and Well-being
Daniel Schmitter, Michael Unser
We provide a generic framework to learn shape dictionaries of landmark-based curves that are defined in the continuous domain. We first present an unbiased alignment method that involves the construction of a mean shape as well as training sets whose elements are subspaces that contain all affine transformations of the training samples. The alignment relies on orthogonal projection operators that have a closed form. We then present algorithms to learn shape dictionaries according to the structure of the data that needs to be encoded: a) projectionbased functional principal-component analysis for homogeneous data and b) continuous-domain sparse shape encoding to learn dictionaries that contain imbalanced data, outliers, or different types of shape structures...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Il Yong Chun, Jeffrey A Fessler
Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems...
October 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Zhao Zhang, Mingbo Zhao, Fanzhang Li, Li Zhang, Shuicheng Yan
We propose a robust Alternating Low-Rank Representation (ALRR) model formed by an alternating forward-backward representation process. For forward representation, ALRR first recovers the low-rank PCs and random corruptions by an adaptive local Robust PCA (RPCA). Then, ALRR performs a joint Lp-norm and L2,p-norm minimization (0<p <1) based sparse LRR by taking the low-rank PCs as inputs and dictionary instead of using the original noisy data to learn the coding coefficients for subspace recovery, where the Lp-norm on the coefficients can ensure joint sparsity for subspace representation, while the L2,p-norm on the reconstruction error can handle outlier pursuit...
September 14, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Naveed Akhtar, Ajmal Mian
We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative...
October 3, 2017: IEEE Transactions on Neural Networks and Learning Systems
Ti Bai, Hao Yan, Xun Jia, Steve Jiang, Ge Wang, Xuanqin Mou
Despite the rapid developments of x-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this work, a sparse constraint based on the 3D dictionary is incorporated into a regularized iterative reconstruction framework, defining the 3DDL method. In addition, by analyzing the sparsity level curve associated with different regularization parameters, a new adaptive parameter selection strategy is proposed to facilitate our 3DDL method...
October 5, 2017: IEEE Transactions on Medical Imaging
Weihong Deng, Jiani Hu, Jun Guo
Collaborative representation methods, such as sparse subspace clustering (SSC) and sparse representation-based classification (SRC), have achieved great success in face clustering and classification by directly utilizing the training images as the dictionary bases. In this paper, we reveal that the superior performance of collaborative representation relies heavily on the sufficiently large class separability of the controlled face datasets such as Extended Yale B. On the uncontrolled or undersampled dataset, however, collaborative representation suffers from the misleading coefficients of the incorrect classes...
September 29, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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