keyword
MENU ▼
Read by QxMD icon Read
search

representational learning

keyword
https://www.readbyqxmd.com/read/28439523/tsallis-entropy-and-sparse-reconstructive-dictionary-learning-for-exudate-detection-in-diabetic-retinopathy
#1
Vineeta Das, Niladri B Puhan
Computer-assisted automated exudate detection is crucial for large-scale screening of diabetic retinopathy (DR). The motivation of this work is robust and accurate detection of low contrast and isolated hard exudates using fundus imaging. Gabor filtering is first performed to enhance exudate visibility followed by Tsallis entropy thresholding. The obtained candidate exudate pixel map is useful for further removal of falsely detected candidates using sparse-based dictionary learning and classification. Two reconstructive dictionaries are learnt using the intensity, gradient, local energy, and transform domain features extracted from exudate and background patches of the training fundus images...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28439250/differential-impact-of-visuospatial-working-memory-on-rule-based-and-information-integration-category-learning
#2
Qiang Xing, Hailong Sun
Previous studies have indicated that the category learning system is a mechanism with multiple processing systems, and that working memory has different effects on category learning. But how does visuospatial working memory affect perceptual category learning? As there is no definite answer to this question, we conducted three experiments. In Experiment 1, the dual-task paradigm with sequential presentation was adopted to investigate the influence of visuospatial working memory on rule-based and information-integration category learning...
2017: Frontiers in Psychology
https://www.readbyqxmd.com/read/28438706/towards-generalizable-entity-centric-clinical-coreference-resolution
#3
Timothy Miller, Dmitriy Dligach, Steven Bethard, Chen Lin, Guergana Savova
OBJECTIVE: This work investigates the problem of clinical coreference resolution in a model that explicitly tracks entities, and aims to measure the performance of that model in both traditional in-domain train/test splits and cross-domain experiments that measure the generalizability of learned models. METHODS: The two methods we compare are a baseline mention-pair coreference system that operates over pairs of mentions with best-first conflict resolution and a mention-synchronous system that incrementally builds coreference chains...
April 21, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28437797/central-and-peripheral-vision-for-scene-recognition-a-neurocomputational-modeling-exploration
#4
Panqu Wang, Garrison W Cottrell
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is more efficient for scene recognition than peripheral, based on the amount of visual area needed for accurate recognition. In this study, we model and explain the results of Larson and Loschky (2009) using a neurocomputational modeling approach. We show that the advantage of peripheral vision in scene recognition, as well as the efficiency advantage for central vision, can be replicated using state-of-the-art deep neural network models...
April 1, 2017: Journal of Vision
https://www.readbyqxmd.com/read/28437486/prediction-of-crime-occurrence-from-multi-modal-data-using-deep-learning
#5
Hyeon-Woo Kang, Hang-Bong Kang
In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets...
2017: PloS One
https://www.readbyqxmd.com/read/28436905/robust-structured-nonnegative-matrix-factorization-for-image-representation
#6
Zechao Li, Jinhui Tang, Xiaofei He
Dimensionality reduction has attracted increasing attention, because high-dimensional data have arisen naturally in numerous domains in recent years. As one popular dimensionality reduction method, nonnegative matrix factorization (NMF), whose goal is to learn parts-based representations, has been widely studied and applied to various applications. In contrast to the previous approaches, this paper proposes a novel semisupervised NMF learning framework, called robust structured NMF, that learns a robust discriminative representation by leveraging the block-diagonal structure and the ℓ2,p-norm (especially when 0 < p ≤ q 1) loss function...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436903/robust-multiview-data-analysis-through-collective-low-rank-subspace
#7
Zhengming Ding, Yun Fu
Multiview data are of great abundance in real-world applications, since various viewpoints and multiple sensors desire to represent the data in a better way. Conventional multiview learning methods aimed to learn multiple view-specific transformations meanwhile assumed the view knowledge of training, and test data were available in advance. However, they would fail when we do not have any prior knowledge for the probe data's view information, since the correct view-specific projections cannot be utilized to extract effective feature representations...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436901/on-better-exploring-and-exploiting-task-relationships-in-multitask-learning-joint-model-and-feature-learning
#8
Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao
Multitask learning (MTL) aims to learn multiple tasks simultaneously through the interdependence between different tasks. The way to measure the relatedness between tasks is always a popular issue. There are mainly two ways to measure relatedness between tasks: common parameters sharing and common features sharing across different tasks. However, these two types of relatedness are mainly learned independently, leading to a loss of information. In this paper, we propose a new strategy to measure the relatedness that jointly learns shared parameters and shared feature representations...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436897/tensor-factorized-neural-networks
#9
Jen-Tzung Chien, Yi-Ting Bao
The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations. The classification performance using vectorized NN is constrained, because the temporal or spatial information in neighboring ways is disregarded. More parameters are required to learn the complicated data structure...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436867/rscm-region-selection-and-concurrency-model-for-multi-class-weather-classification
#10
Di Lin, Cewu Lu, Hui Huang, Jiaya Jia
Towards weather condition recognition, we emphasize the importance of regional cues in this paper and address a few important problems regarding appropriate representation, its differentiation among regions, and weather-condition feature construction. Our major contribution is, first, to construct a multi-class benchmark dataset containing 65,000 images from 6 common categories for sunny, cloudy, rainy, snowy, haze and thunder weather. This dataset also benefits weather classification and attribute recognition...
April 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28436862/part-based-deep-hashing-for-large-scale-person-re-identification
#11
Fuqing Zhu, Xiangwei Kong, Liang Zheng, Haiyan Fu, Qi Tian
Large scale is a trend in person re-identification (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person reidentification. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture...
April 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28436845/drawing-and-recognizing-chinese-characters-with-recurrent-neural-network
#12
Xu-Yao Zhang, Fei Yin, Yan-Ming Zhang, Cheng-Lin Liu, Yoshua Bengio
Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters...
April 18, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28436664/cppred-rf-a-sequence-based-predictor-for-identifying-cell-penetrating-peptides-and-their-uptake-efficiency
#13
Leyi Wei, Pengwei Xing, Ran Su, Gaotao Shi, Zhanshan Sam Ma, Quan Zou
Cell-penetrating peptides (CPPs), have been proven as important drug delivery vehicles, demonstrating the potential as therapeutic candidates. The last decade has witnessed a rapid growth in CPP-based research. Recently, many computational efforts have been made to develop machine learning based methods for identifying CPPs. Although much progress has been made, existing methods still suffer low feature representation capability that limits further performance improvement. In this study, we propose a novel predictor called CPPred-RF, in which we integrate multiple sequence-based feature descriptors to sufficiently explore distinct information embedded in CPPs, employ a well-established feature selection technique to improve the feature representation, and at the first time, construct a 2-layer prediction framework based on the random forest algorithm...
April 24, 2017: Journal of Proteome Research
https://www.readbyqxmd.com/read/28434455/creating-a-pediatric-joint-council-to-promote-patient-safety-and-quality-governance-and-accountability-across-johns-hopkins-medicine
#14
Michael Rosen, Brigitta U Mueller, Aaron M Milstone, Denise R Remus, Renee Demski, Peter J Pronovost, Marlene R Miller
BACKGROUND: Large multihospital health systems with multiple children's hospitals are relatively few in number. With a paucity of national pediatric measures for quality and patient safety, there are unique challenges to ensuring consistent levels of care across diverse health care delivery settings. At Johns Hopkins Medicine, a Pediatric Joint Council was created to help ensure high-quality and safe care across a health system encompassing two full-service children's hospitals and two community hospitals with significant pediatric volumes across two states...
May 2017: Joint Commission Journal on Quality and Patient Safety
https://www.readbyqxmd.com/read/28434153/machine-learning-xgboost-analysis-of-language-networks-to-classify-patients-with-epilepsy
#15
L Torlay, M Perrone-Bertolotti, E Thomas, M Baciu
Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing 'atypical' (compared to 'typical' in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures...
April 22, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28432767/categorical-learning-revealed-in-activity-pattern-of-left-fusiform-cortex
#16
Jessica E Goold, Ming Meng
The brain is organized such that it encodes and maintains category information about thousands of objects. However, how learning shapes these neural representations of object categories is unknown. The present study focuses on faces, examining whether: (1) Enhanced categorical discrimination or (2) Feature analysis enhances face/non-face categorization in the brain. Stimuli ranged from non-faces to faces with two-toned Mooney images used for testing and gray-scale images used for training. The stimulus set was specifically chosen because it has a true categorical boundary between faces and non-faces but the stimuli surrounding that boundary have very similar features, making the boundary harder to learn...
April 22, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28432316/how-visual-experience-impacts-the-internal-and-external-spatial-mapping-of-sensorimotor-functions
#17
Virginie Crollen, Geneviève Albouy, Franco Lepore, Olivier Collignon
Tactile perception and motor production share the use of internally- and externally-defined coordinates. In order to examine how visual experience affects the internal/external coding of space for touch and movement, early blind (EB) and sighted controls (SC) took part in two experiments. In experiment 1, participants were required to perform a Temporal Order Judgment task (TOJ), either with their hands in parallel or crossed over the body midline. Confirming previous demonstration, crossing the hands led to a significant decrement in performance in SC but did not affect EB...
April 21, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28427407/constructive-ehealth-evaluation-lessons-from-evaluation-of-ehr-development-in-4-danish-hospitals
#18
Anna Marie Balling Høstgaard, Pernille Bertelsen, Christian Nøhr
BACKGROUND: Information and communication sources in the healthcare sector are replaced with new eHealth technologies. This has led to problems arising from the lack of awareness of the importance of end-user involvement in eHealth development and of the difficulties caused by using traditional summative evaluation methods. The Constructive eHealth evaluation method (CeHEM) provides a solution to these problems by offering an evaluation framework for supporting and facilitating end-user involvement during all phases of eHealth development...
April 20, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28423783/automated-diagnosis-coding-with-combined-text-representations
#19
Stefan Berndorfer, Aron Henriksson
Automated diagnosis coding can be provided efficiently by learning predictive models from historical data; however, discriminating between thousands of codes while allowing a variable number of codes to be assigned is extremely difficult. Here, we explore various text representations and classification models for assigning ICD-9 codes to discharge summaries in MIMIC-III. It is shown that the relative effectiveness of the investigated representations depends on the frequency of the diagnosis code under consideration and that the best performance is obtained by combining models built using different representations...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423773/introducing-a-method-for-transformation-of-paper-based-research-data-into-concept-based-representation-with-openehr
#20
Birgit Saalfeld, Erik Tute, Klaus-Hendrik Wolf, Michael Marschollek
Combining research data and clinical routine data is a chance for medical research. We present our method for the transformation of paper-based research data into a concept-based representation. With this representation the study data from research projects can be combined with data from clinical tools with less integration effort. We applied and verified our method using data from a current research study. In this paper we also show our main challenges and lessons learned. Clinical assessment data and study diaries from a long term study (n=24, 3 months observation time each, 17 different clinical assessments) stored on paper were used as the data set...
2017: Studies in Health Technology and Informatics
keyword
keyword
74784
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"