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https://www.readbyqxmd.com/read/28231405/three-dimensional-printing-of-x-ray-computed-tomography-datasets-with-multiple-materials-using-open-source-data-processing
#1
Ian M Sander, Matthew T McGoldrick, My N Helms, Aislinn Betts, Anthony van Avermaete, Elizabeth Owers, Evan Doney, Taimi Liepert, Glen Niebur, Douglas Liepert, W Matthew Leevy
Advances in three-dimensional (3D) printing allow for digital files to be turned into a "printed" physical product. For example, complex anatomical models derived from clinical or pre-clinical X-ray computed tomography (CT) data of patients or research specimens can be constructed using various printable materials. Although 3D printing has the potential to advance learning, many academic programs have been slow to adopt its use in the classroom despite increased availability of the equipment and digital databases already established for educational use...
February 23, 2017: Anatomical Sciences Education
https://www.readbyqxmd.com/read/28230848/computational-approaches-to-fmri-analysis
#2
Jonathan D Cohen, Nathaniel Daw, Barbara Engelhardt, Uri Hasson, Kai Li, Yael Niv, Kenneth A Norman, Jonathan Pillow, Peter J Ramadge, Nicholas B Turk-Browne, Theodore L Willke
Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis...
February 23, 2017: Nature Neuroscience
https://www.readbyqxmd.com/read/28230725/towards-a-semantic-web-of-things-a-hybrid-semantic-annotation-extraction-and-reasoning-framework-for-cyber-physical-system
#3
Zhenyu Wu, Yuan Xu, Yunong Yang, Chunhong Zhang, Xinning Zhu, Yang Ji
Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability...
February 20, 2017: Sensors
https://www.readbyqxmd.com/read/28229132/representing-documents-via-latent-keyphrase-inference
#4
Jialu Liu, Xiang Ren, Jingbo Shang, Taylor Cassidy, Clare R Voss, Jiawei Han
Many text mining approaches adopt bag-of-words or n-grams models to represent documents. Looking beyond just the words, i.e., the explicit surface forms, in a document can improve a computer's understanding of text. Being aware of this, researchers have proposed concept-based models that rely on a human-curated knowledge base to incorporate other related concepts in the document representation. But these methods are not desirable when applied to vertical domains (e.g., literature, enterprise, etc.) due to low coverage of in-domain concepts in the general knowledge base and interference from out-of-domain concepts...
April 2016: Proceedings of the International World-Wide Web Conference
https://www.readbyqxmd.com/read/28227913/online-learning-of-gait-models-for-calculation-of-gait-parameters
#5
Jamie L S Waugh, Anton Trinh, Ryan R Mohammed, William E McIlroy, Dana Kulic, Jamie L S Waugh, Anton Trinh, Ryan R Mohammed, William E McIlroy, Dana Kulic, Jamie L S Waugh, Dana Kulic, Anton Trinh, William E McIlroy, Ryan R Mohammed
This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intra-personal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227782/predicting-short-term-icu-outcomes-using-a-sequential-contrast-motif-based-classification-framework
#6
Shameek Ghosh, Hung Nguyen, Jinyan Li, Shameek Ghosh, Hung Nguyen, Jinyan Li, Shameek Ghosh, Hung Nguyen, Jinyan Li
Critical ICU events like acute hypotension and septic shock are dangerous complications, leading to multiple organ failures and eventual death. Previously, pattern mining algorithms have been employed for extracting interesting rules in various clinical domains. However, the extracted rules are directly investigated by clinicians for diagnosing a disease. Towards this purpose, there is a need to develop advanced prediction models which integrate dynamic patterns to learn a patient's physiological condition...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227266/dictionary-learning-for-sparse-representation-and-classification-of-neural-spikes
#7
Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao, Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao, Douglas Weber, Zhi-Hong Mao, Yiran Chen, Ahmed H Dallal
Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering. In our method, we use Fisher discriminant based dictionary learning to learn dictionary, whose sub-dictionaries are class specific, and estimate discriminative sparse coding coefficients by minimizing the within class scatter and maximizing the between class scatter...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227228/multi-view-non-negative-tensor-factorization-as-relation-learning-in-healthcare-data
#8
Hang Wu, May D Wang, Hang Wu, May D Wang, May D Wang, Hang Wu
Discovering patterns in co-occurrences data between objects and groups of concepts is a useful task in many domains, such as healthcare data analysis, information retrieval, and recommender systems. These relational representations come from objects' behaviors in different views, posing a challenging task of integrating information from these views to uncover the shared latent structures. The problem is further complicated by the high dimension of data and the large ratio of missing data. We propose a new paradigm of learning semantic relations using tensor factorization, by jointly factorizing multi-view tensors and searching for a consistent underlying semantic space across each views...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226758/a-deep-bag-of-features-model-for-the-classification-of-melanomas-in-dermoscopy-images
#9
S Sabbaghi, M Aldeen, R Garnavi, S Sabbaghi, M Aldeen, R Garnavi, M Aldeen, S Sabbaghi, R Garnavi
Deep learning and unsupervised feature learning have received great attention in past years for their ability to transform input data into high level representations using machine learning techniques. Such interest has been growing steadily in the field of medical image diagnosis, particularly in melanoma classification. In this paper, a novel application of deep learning (stacked sparse auto-encoders) is presented for skin lesion classification task. The stacked sparse auto-encoder discovers latent information features in input images (pixel intensities)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226735/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#10
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Xinyang Feng, Jie Yang, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226674/a-multiple-kernel-learning-approach-for-human-behavioral-task-classification-using-stn-lfp-signal
#11
Hosein M Golshan, Adam O Hebb, Sara J Hanrahan, Joshua Nedrud, Mohammad H Mahoor, Hosein M Golshan, Adam O Hebb, Sara J Hanrahan, Joshua Nedrud, Mohammad H Mahoor, Joshua Nedrud, Adam O Hebb, Sara J Hanrahan, Mohammad H Mahoor, Hosein M Golshan
Deep Brain Stimulation (DBS) has gained increasing attention as an effective method to mitigate Parkinson's disease (PD) disorders. Existing DBS systems are open-loop such that the system parameters are not adjusted automatically based on patient's behavior. Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop. This paper presents a classification approach to recognize such behavioral tasks using the subthalamic nucleus (STN) Local Field Potential (LFP) signals...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226630/gaussian-process-dynamical-models-for-multimodal-affect-recognition
#12
Hernan F Garcia, Mauricio A Alvarez, Alvaro A Orozco, Hernan F Garcia, Mauricio A Alvarez, Alvaro A Orozco, Mauricio A Alvarez, Alvaro A Orozco, Hernan F Garcia
Affective computing systems has a great potential in applications for biofeedback systems and cognitive conductual therapies. Here, by analyzing the physiological behavior of a given subject, we can infer the affective state of an emotional process. Since, emotions can be modeled as dynamic manifestations of these signals, a continuous analysis in the valence/arousal space, brings more information of the affective state related to an emotional process. In this paper we propose a method for dynamic affect recognition from multimodal physiological signals...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28225796/what-can-we-learn-about-beat-perception-by-comparing-brain-signals-and-stimulus-envelopes
#13
Molly J Henry, Björn Herrmann, Jessica A Grahn
Entrainment of neural oscillations on multiple time scales is important for the perception of speech. Musical rhythms, and in particular the perception of a regular beat in musical rhythms, is also likely to rely on entrainment of neural oscillations. One recently proposed approach to studying beat perception in the context of neural entrainment and resonance (the "frequency-tagging" approach) has received an enthusiastic response from the scientific community. A specific version of the approach involves comparing frequency-domain representations of acoustic rhythm stimuli to the frequency-domain representations of neural responses to those rhythms (measured by electroencephalography, EEG)...
2017: PloS One
https://www.readbyqxmd.com/read/28223187/deepnat-deep-convolutional-neural-network-for-segmenting-neuroanatomy
#14
Christian Wachinger, Martin Reuter, Tassilo Klein
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground...
February 18, 2017: NeuroImage
https://www.readbyqxmd.com/read/28222000/fast-prediction-of-protein-methylation-sites-using-a-sequence-based-feature-selection-technique
#15
Leyi Wei, Pengwei Xing, Gaotao Shi, Zhi-Liang Ji, Quan Zou
Protein methylation, an important post-translational modification, plays crucial roles in many cellular processes. The accurate prediction of protein methylation sites is fundamentally important for revealing the molecular mechanisms undergoing methylation. In recent years, computational prediction based on machine learning algorithms has emerged as a powerful and robust approach for identifying methylation sites, and much progress has been made in predictive performance improvement. However, the predictive performance of existing methods is not satisfactory in terms of overall accuracy...
February 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28221996/hierarchical-latent-concept-discovery-for-video-event-detection
#16
Chao Li, Zi Huang, Yang Yang, Jiewei Cao, Xiaoshui Sun, Heng Tao Shen
Semantic information is important for video event detection. How to automatically discover, model and utilize semantic information to facilitate video event detection has been a challenging problem. In this paper, we propose a novel hierarchical video event detection model, which deliberately unifies the processes of underlying semantics discovery and event modelling from video data. Specially, different from most approaches based on manually pre-defined concepts, we devise an effective model to automatically uncover video semantics by hierarchically capturing latent static-visual concepts in frame-level and latent activity concepts (i...
February 17, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28215558/optimal-degrees-of-synaptic-connectivity
#17
Ashok Litwin-Kumar, Kameron Decker Harris, Richard Axel, Haim Sompolinsky, L F Abbott
Synaptic connectivity varies widely across neuronal types. Cerebellar granule cells receive five orders of magnitude fewer inputs than the Purkinje cells they innervate, and cerebellum-like circuits, including the insect mushroom body, also exhibit large divergences in connectivity. In contrast, the number of inputs per neuron in cerebral cortex is more uniform and large. We investigate how the dimension of a representation formed by a population of neurons depends on how many inputs each neuron receives and what this implies for learning associations...
February 16, 2017: Neuron
https://www.readbyqxmd.com/read/28214787/sparse-and-dense-hybrid-representation-via-subspace-modeling-for-dynamic-mri
#18
Qiegen Liu, Shanshan Wang, Dong Liang
Recent theoretical results on compressed sensing and low-rank matrix recovery have inspired significant interest in joint sparse and low rank modeling of dynamic magnetic resonance imaging (dMRI). Existing approaches usually describe these two respective prior information with different formulations. In this paper, we present a novel sparse and dense hybrid representation (SDR) model which describes the sparse plus low rank properties by a unified way. More specifically, under the learned dictionary consisting of temporal basis functions, SDR models the spatial coefficients in two subspaces with Laplacian and Gaussian prior distributions, respectively...
February 5, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28212388/multiple-processes-in-two-dimensional-visual-statistical-learning
#19
Eiichi Hoshino, Ken Mogi
Knowledge about the arrangement of visual elements is an important aspect of perception. This study investigates whether humans learn rules of two-dimensional abstract patterns (exemplars) generated from Reber's artificial grammar. The key question is whether the subjects can implicitly learn them without explicit instructions, and, if so, how they use the acquired knowledge to judge new patterns (probes) in relation to their finite experience of the exemplars. The analysis was conducted using dissimilarities among patterns, which are defined with n-gram probabilities and the Levenshtein distance...
2017: PloS One
https://www.readbyqxmd.com/read/28212104/joint-feature-selection-and-classification-for-multilabel-learning
#20
Jun Huang, Guorong Li, Qingming Huang, Xindong Wu
Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels...
February 14, 2017: IEEE Transactions on Cybernetics
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