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https://www.readbyqxmd.com/read/28922127/jointly-learning-structured-analysis-discriminative-dictionary-and-analysis-multiclass-classifier
#1
Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, Shuicheng Yan
In this paper, we propose an analysis mechanism-based structured analysis discriminative dictionary learning (ADDL) framework. The ADDL seamlessly integrates ADDL, analysis representation, and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learned dictionaries, representations, and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the subdictionaries associated with different classes to be independent...
September 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28922120/motion-estimation-in-echocardiography-using-sparse-representation-and-dictionary-learning
#2
Nora Ouzir, Adrian Basarab, Herve Liebgott, Brahim Harbaoui, Jean-Yves Tourneret
This paper introduces a new method for cardiac motion estimation in 2D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one dataset with available ground-truth, including four sequences of highly realistic simulations...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28922118/a-fast-gradient-method-for-nonnegative-sparse-regression-with-self-dictionary
#3
Nicolas Gillis, Robert Luce
A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these conic basis columns are based on nonnegative sparse regression and self dictionaries, and require the solution of large-scale convex optimization problems. In this paper we study a particular nonnegative sparse regression model with self dictionary...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28920914/robust-visual-tracking-via-online-discriminative-and-low-rank-dictionary-learning
#4
Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang
In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank...
September 12, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28919923/revealing-topics-and-their-evolution-in-biomedical-literature-using-bio-dtm-a-case-study-of-ginseng
#5
Qian Chen, Ni Ai, Jie Liao, Xin Shao, Yufeng Liu, Xiaohui Fan
BACKGROUND: Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. METHODS: The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization...
2017: Chinese Medicine
https://www.readbyqxmd.com/read/28915341/-31-p-magnetic-resonance-fingerprinting-for-rapid-quantification-of-creatine-kinase-reaction-rate-in-vivo
#6
Charlie Y Wang, Yuchi Liu, Shuying Huang, Mark A Griswold, Nicole Seiberlich, Xin Yu
The purpose of this work was to develop a (31) P spectroscopic magnetic resonance fingerprinting (MRF) method for fast quantification of the chemical exchange rate between phosphocreatine (PCr) and adenosine triphosphate (ATP) via creatine kinase (CK). A (31) P MRF sequence (CK-MRF) was developed to quantify the forward rate constant of ATP synthesis via CK ( kfCK), the T1 relaxation time of PCr ( T1PCr), and the PCr-to-ATP concentration ratio ( MRPCr). The CK-MRF sequence used a balanced steady-state free precession (bSSFP)-type excitation with ramped flip angles and a unique saturation scheme sensitive to the exchange between PCr and γATP...
September 15, 2017: NMR in Biomedicine
https://www.readbyqxmd.com/read/28914787/a-sparse-dictionary-learning-based-adaptive-patch-inpainting-method-for-thick-clouds-removal-from-high-spatial-resolution-remote-sensing-imagery
#7
Fan Meng, Xiaomei Yang, Chenghu Zhou, Zhi Li
Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation...
September 15, 2017: Sensors
https://www.readbyqxmd.com/read/28910766/light-field-compression-with-disparity-guided-sparse-coding-based-on-structural-key-views
#8
Jie Chen, Junhui Hou, Lap-Pui Chau
Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. One of the emerging technologies is light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and transmission bandwidth are required by a LF image as compared with a conventional 2D image of similar spatial dimension. Hence, the compression of LF data becomes a vital part of its application. In this paper, we propose a LF codec with disparity guided Sparse Coding over a learned perspective-shifted LF dictionary based on selected Structural Key Views (SC-SKV)...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910696/tissue-microstructure-estimation-using-a-deep-network-inspired-by-a-dictionary-based-framework
#9
Chuyang Ye
Diffusion magnetic resonance imaging (dMRI) captures the anisotropic pattern of water displacement in the neuronal tissue and allows noninvasive investigation of the complex tissue microstructure. A number of biophysical models have been proposed to relate the tissue organization with the observed diffusion signals, so that the tissue microstructure can be inferred. The Neurite Orientation Dispersion and Density Imaging (NODDI) model has been a popular choice and has been widely used for many neuroscientific studies...
September 6, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28883201/deep-learning-for-magnetic-resonance-fingerprinting-a-new-approach-for-predicting-quantitative-parameter-values-from-time-series
#10
Elisabeth Hoppe, Gregor Körzdörfer, Tobias Würfl, Jens Wetzl, Felix Lugauer, Josef Pfeuffer, Andreas Maier
The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady state signal is generated by a pseudo-random excitation pattern. A comparison of the measured signal in each voxel with the physical model yields quantitative parameter maps. Currently, the comparison is done by matching a dictionary of simulated signals to the acquired signals...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28882419/carcinoma-in-situ-of-the-breast-in-new-south-wales-australia-current-status-and-trends-over-the-last-40-year
#11
Gemma Jacklyn, Stephen Morrell, Kevin McGeechan, Nehmat Houssami, Les Irwig, Nirmala Pathmanathan, Alexandra Barratt
BACKGROUND: The incidence of non-invasive breast cancer has increased substantially over time. We aim to describe temporal trends in the incidence of carcinoma in situ of the breast in New South Wales (NSW), Australia. METHODS: Descriptive study of trends in the incidence of ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) in women who received a diagnosis from 1972 to 2012, recorded in the NSW Cancer Registry. RESULTS: Carcinoma in situ as a proportion of all breast cancer was 0...
September 4, 2017: Breast: Official Journal of the European Society of Mastology
https://www.readbyqxmd.com/read/28881963/deep-learning-with-word-embeddings-improves-biomedical-named-entity-recognition
#12
Maryam Habibi, Leon Weber, Mariana Neves, David Luis Wiegandt, Ulf Leser
Motivation: Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly...
July 15, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28874581/discrete-modes-of-social-information-processing-predict-individual-behavior-of-fish-in-a-group
#13
Roy Harpaz, Gašper Tkačik, Elad Schneidman
Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior...
September 5, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28873056/foreground-detection-with-simultaneous-dictionary-learning-and-historical-pixel-maintenance
#14
Pei Dong, Shanshan Wang, Yong Xia, Dong Liang, David Dagan Feng
Foreground detection is fundamental in surveillance video analysis and meaningful toward object tracking and higher level tasks, such as anomaly detection and activity analysis. Nevertheless, existing methods are still limited in accurately detecting the foreground due to the complex scene settings. To robustly handle the diverse background variations and foreground challenges, this paper proposes a Background REpresentation approach With Dictionary Learning and Historical Pixel Maintenance (BREW-DLHPM). Specifically, a dictionary learning problem is formulated at the frame level to adaptively represent the background signals with the varied structure information captured, while a pixel-level maintenance is exploited to grasp the dynamic nature of historical information under the help of the learned background...
November 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28868150/electrocardiograph-signal-denoising-based-on-sparse-decomposition
#15
Junjiang Zhu, Xiaolu Li
Noise in ECG signals will affect the result of post-processing if left untreated. Since ECG is highly subjective, the linear denoising method with a specific threshold working well on one subject could fail on another. Therefore, in this Letter, sparse-based method, which represents every segment of signal using different linear combinations of atoms from a dictionary, is used to denoise ECG signals, with a view to myoelectric interference existing in ECG signals. Firstly, a denoising model for ECG signals is constructed...
August 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28867440/etonogestrel-implant-migration-to-the-vasculature-chest-wall-and-distant-body-sites-cases-from-a-pharmacovigilance-database
#16
Sarah Kang, Ali Niak, Neha Gada, Allen Brinker, S Christopher Jones
OBJECTIVE: To describe clinical outcomes of etonogestrel implant patients with migration to the vasculature, chest wall, and other distant body sites spontaneously reported to the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. STUDY DESIGN: We performed a standardized Medical Dictionary for Regulatory Activities (MedDRA) query in the FAERS database (through November 15, 2015) with reports coded with one or more MedDRA preferred terms (PTs) that indicate complications with device placement or migration of the device from the original site of insertion to the vasculature, chest wall and other distant body sites...
August 31, 2017: Contraception
https://www.readbyqxmd.com/read/28866519/an-intelligent-system-approach-for-probabilistic-volume-rendering-using-hierarchical-3d-convolutional-sparse-coding
#17
Tran Minh Quan, Junyoung Choi, Haejin Jeong, Won-Ki Jeong
In this paper, we propose a novel machine learning-based voxel classification method for highly-accurate volume rendering. Unlike conventional voxel classification methods that incorporate intensity-based features, the proposed method employs dictionary based features learned directly from the input data using hierarchical multi-scale 3D convolutional sparse coding, a novel extension of the state-of-the-art learning-based sparse feature representation method. The proposed approach automatically generates highdimensional feature vectors in up to 75 dimensions, which are then fed into an intelligent system built on a random forest classifier for accurately classifying voxels from only a handful of selection scribbles made directly on the input data by the user...
August 29, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28859825/multi-modal-discriminative-dictionary-learning-for-alzheimer-s-disease-and-mild-cognitive-impairment
#18
Qing Li, Xia Wu, Lele Xu, Kewei Chen, Li Yao, Rui Li
BACKGROUND AND OBJECTIVE: The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from normal control (NC) is important as the recent research emphasis on early pre-clinical stage for possible disease abnormality identification, intervention and even possible prevention. METHODS: The current study puts forward a multi-modal supervised within-class-similarity discriminative dictionary learning algorithm (SCDDL) we introduced previously for distinguishing MCI from NC...
October 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28858805/optimal-couple-projections-for-domain-adaptive-sparse-representation-based-classification
#19
Guoqing Zhang, Huaijiang Sun, Fatih Porikli, Yazhou Liu, Quansen Sun
In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space...
August 29, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28844318/tolerability-of-the-sq-tree-slit-tablet-in-adults
#20
Anne O Birk, Jens S Andersen, Hanne H Villesen, Maria A Steffensen, Moises A Calderon
PURPOSE: The tree pollen sublingual immunotherapy (SLIT)-tablet (ALK, Denmark) is being developed for the treatment of tree pollen induced allergic rhinitis with or without conjunctivitis. The objective of this Phase I trial was to investigate the tolerability and acceptable dose range of the SQ tree SLIT-tablet in adults with allergic rhinoconjunctivitis. METHODS: The trial was a randomized, double-blind, placebo-controlled, dose escalation Phase I trial that included 70 adults (aged 19-61 years) with birch pollen-induced rhinoconjunctivitis with or without mild to moderate asthma...
August 22, 2017: Clinical Therapeutics
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