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Dictionary learning

Ryan Eshleman, Rahul Singh
BACKGROUND: Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention...
October 6, 2016: BMC Bioinformatics
Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad
This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first updated the dictionary using the method of optimal directions (MOD) and then applied a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of the column-normalized least squares estimate of the dictionary obtained from the MOD step. We then shrink the rank of this learned dictionary by transforming the problem of reducing the rank to a nonnegative garrotte estimation problem and solving it using a path-wise coordinate descent approach...
October 20, 2016: Neural Computation
Shuai Liu, Licheng Jiao, Shuyuan Yang
During the acquisition process hyperspectral images (HSI) are inevitably corrupted by various noises, which greatly influence their visual impression and subsequent applications. In this paper, a novel Bayesian approach integrating hierarchical sparse learning and spectral-spatial information is proposed for HSI denoising. Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset. To exploit local similarity, each subset is then divided into overlapping cubic patches...
October 17, 2016: Sensors
Christoph Ahlgrim, Oliver Maenner, Manfred W Baumstark
BACKGROUND: Speech recognition software might increase productivity in clinical documentation. However, low user satisfaction with speech recognition software has been observed. In this case study, an approach for implementing a speech recognition software package at a university-based outpatient department is presented. METHODS: Methods to create a specific dictionary for the context "sports medicine" and a shared vocabulary learning function are demonstrated. The approach is evaluated for user satisfaction (using a questionnaire before and 10 weeks after software implementation) and its impact on the time until the final medical document was saved into the system...
October 18, 2016: BMC Medical Informatics and Decision Making
Shanshan Wang, Jianbo Liu, Xi Peng, Pei Dong, Qiegen Liu, Dong Liang
Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame...
2016: BioMed Research International
Xiaodong Zhang, Shasha Jing, Peiyi Gao, Jing Xue, Lu Su, Weiping Li, Lijie Ren, Qingmao Hu
Segmentation of infarcts at hyperacute stage is challenging as they exhibit substantial variability which may even be hard for experts to delineate manually. In this paper, a sparse representation based classification method is explored. For each patient, four volumetric data items including three volumes of diffusion weighted imaging and a computed asymmetry map are employed to extract patch features which are then fed to dictionary learning and classification based on sparse representation. Elastic net is adopted to replace the traditional L0-norm/L1-norm constraints on sparse representation to stabilize sparse code...
2016: Computational and Mathematical Methods in Medicine
Bo Ma, Lianghua Huang, Jianbing Shen, Ling Shao, Ming-Hsuan Yang, Fatih Porikli
Most existing tracking algorithms do not explicitly consider the motion blur contained in video sequences, which degrades their performance in real world applications where motion blur often occurs. In this paper, we propose to solve the motion blur problem in visual tracking in a unified framework. Specifically, a joint blur state estimation and multi-task reverse sparse learning framework is presented, where the closed-form solution of blur kernel and sparse code matrix are obtained simultaneously. The reverse process considers the blurry candidates as dictionary elements, and sparsely represents blurred templates with the candidates...
October 6, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yudan Ren, Jun Fang, Jinglei Lv, Xintao Hu, Cong Christine Guo, Lei Guo, Jiansong Xu, Marc N Potenza, Tianming Liu
Assessing functional brain activation patterns in neuropsychiatric disorders such as cocaine dependence (CD) or pathological gambling (PG) under naturalistic stimuli has received rising interest in recent years. In this paper, we propose and apply a novel group-wise sparse representation framework to assess differences in neural responses to naturalistic stimuli across multiple groups of participants (healthy control, cocaine dependence, pathological gambling). Specifically, natural stimulus fMRI (N-fMRI) signals from all three groups of subjects are aggregated into a big data matrix, which is then decomposed into a common signal basis dictionary and associated weight coefficient matrices via an effective online dictionary learning and sparse coding method...
October 4, 2016: Brain Imaging and Behavior
Donghao Wang, Jiangwen Wan, Junying Chen, Qiang Zhang
To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure...
2016: Sensors
Sikiru Afolabi Adebileje, Keyvan Ghasemi, Hammed Tanimowo Aiyelabegan, Hamidreza Saligheh Rad
Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods...
September 23, 2016: Magnetic Resonance in Chemistry: MRC
Xin Yuan, Xuejun Liao, Patrick Llull, David Brady, Lawrence Carin
We present efficient camera hardware and algorithms to capture images with extended depth of field. The camera moves its focal plane via a liquid lens and modulates the scene at different focal planes by shifting a fixed binary mask, with synchronization achieved by using the same triangular wave to control the focal plane and the pizeoelectronic translator that shifts the mask. Efficient algorithms are developed to reconstruct the all-in-focus image and the depth map from a single coded exposure, and various sparsity priors are investigated to enhance the reconstruction, including group sparsity, tree structure, and dictionary learning...
September 20, 2016: Applied Optics
Zijing Chen, Xinge You, Boxuan Zhong, Jun Li, Dacheng Tao
Visual tracking is a critical task in many computer vision applications such as surveillance and robotics. However, although the robustness to local corruptions has been improved, prevailing trackers are still sensitive to large scale corruptions, such as occlusions and illumination variations. In this paper, we propose a novel robust object tracking technique depends on subspace learning-based appearance model. Our contributions are twofold. First, mask templates produced by frame difference are introduced into our template dictionary...
September 7, 2016: IEEE Transactions on Cybernetics
Xiumei Tian, Dong Zeng, Shanli Zhang, Jing Huang, Hua Zhang, Ji He, Lijun Lu, Weiwen Xi, Jianhua Ma, Zhaoying Bian
Dynamic cerebral perfusion x-ray computed tomography (PCT) imaging has been advocated to quantitatively and qualitatively assess hemodynamic parameters in the diagnosis of acute stroke or chronic cerebrovascular diseases. However, the associated radiation dose is a significant concern to patients due to its dynamic scan protocol. To address this issue, in this paper we propose an image restoration method by utilizing coupled dictionary learning (CDL) scheme to yield clinically acceptable PCT images with low-dose data acquisition...
September 3, 2016: Journal of X-ray Science and Technology
Idit Diamant, Eyal Klang, Michal Amitai, Eli Konen, Jacob Goldberger, Hayit Greenspan
OBJECTIVE: We present a novel variant of the Bagof- Visual-Words (BoVW) method for automated medical image classification. METHODS: Our approach improves the BoVW model by learning a task driven dictionary of the most relevant visual words per task using a mutual information based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words...
September 1, 2016: IEEE Transactions on Bio-medical Engineering
Luoluo Liu, Jeffrey Glaister, Xiaoxia Sun, Aaron Carass, Trac D Tran, Jerry L Prince
Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation...
February 27, 2016: Proceedings of SPIE
Xinzheng Zhang, Qiuyue Yang, Miaomiao Liu, Yunjian Jia, Shujun Liu, Guojun Li
Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS) sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning...
2016: Sensors
Mahdad Esmaeili, Alireza Mehri Dehnavi, Hossein Rabbani, Fedra Hajizadeh
This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinal cysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherence tomography (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) system, and show the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximate the corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce a new scheme in dictionary learning and take curvelet transform of filtered image then denoise and modify each noisy coefficients matrix in each scale with predefined initial 3D sparse dictionary...
July 2016: Journal of Medical Signals and Sensors
Saliha Minhas, Amir Hussain
Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar research, this paper extracts three distinct sets of features from a newly constructed corpus of narratives (408 annual reports/10-K, 6.5 million words) from fraud and non-fraud firms. Separately each of these three sets of features is put through a suite of classification algorithms, to determine classifier performance in this binary fraud/non-fraud discrimination task...
2016: Cognitive Computation
Shu Zhang, Yingying Zhu, Amit Roy Chowdhury
We present a method that is able to find the most informative video portions, leading to a summarization of video sequences. In contrast to the existing works, our method is able to capture the important video portions through information about individual local motion regions, as well as the interactions between these motion regions. Specifically, our proposed Context-Aware Video Summarization (CAVS) framework adopts the methodology of sparse coding with generalized sparse group lasso to learn a dictionary of video features and a dictionary of spatio-temporal feature correlation graphs...
August 18, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Shahab Ensafi, Shijian Lu, Ashraf A Kassim, Chew Lim Tan
Autoimmune diseases (AD) are the abnormal response of the immune system of the body to healthy tissues. ADs have generally been on the increase. Efficient computer aided diagnosis of ADs through classification of the human epithelial type 2 (HEp-2) cells become beneficial. These methods make lower diagnosis costs, faster response and better diagnosis repeatability. In this paper, we present an automated HEp-2 cell image classification technique that exploits the sparse coding of the visual features together with the Bag of Words model (SBoW)...
August 8, 2016: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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