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https://www.readbyqxmd.com/read/29018640/a-multi-classifier-system-for-automatic-mitosis-detection-in-breast-histopathology-images-using-deep-belief-networks
#1
K Sabeena Beevi, Madhu S Nair, G R Bindu
Mitotic count is an important diagnostic factor in breast cancer grading and prognosis. Detection of mitosis in breast histopathology images is very challenging mainly due to diffused intensities along object boundary and shape variation in different stages of mitosis. This paper demonstrates an accurate technique for detecting the mitotic cells in Hematoxyline and Eosin stained images by step by step refinement of segmentation and classification stages. Krill Herd Algorithm-based localized active contour model precisely segments cell nuclei from background stroma...
2017: IEEE Journal of Translational Engineering in Health and Medicine
https://www.readbyqxmd.com/read/28991060/hemodynamic-instability-and-cardiovascular-events-after-traumatic-brain-injury-predict-outcome-after-artifact-removal-with-deep-belief-network-analysis
#2
Hakseung Kim, Seung-Bo Lee, Yunsik Son, Marek Czosnyka, Dong-Joo Kim
BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals. A machine-learning model for the elimination of artifactual events shows promising results for improving signal quality. However, the actual impact of the improvements on the performance of the clinical parameters after the elimination of the artifacts is not well studied...
October 5, 2017: Journal of Neurosurgical Anesthesiology
https://www.readbyqxmd.com/read/28946991/deep-learning-ensemble-with-asymptotic-techniques-for-oscillometric-blood-pressure-estimation
#3
Soojeong Lee, Joon-Hyuk Chang
BACKGROUND AND OBJECTIVE: This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. METHOD: This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements...
November 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28919042/new-tools-for-the-visualization-of-biological-pathways
#4
Tomojit Ghosh, Xiaofeng Ma, Michael Kirby
This paper presents several geometrically motivated techniques for the visualization of high-dimensional biological data sets. The Grassmann manifold provides a robust framework for measuring data similarity in a subspace context. Sparse radial basis function classification as a visualization technique leverages recent advances in radial basis function learning via convex optimization. In the spirit of deep belief networks, supervised centroid-encoding is proposed as a way to exploit class label information...
September 15, 2017: Methods: a Companion to Methods in Enzymology
https://www.readbyqxmd.com/read/28875051/statistics-and-deep-belief-network-based-cardiovascular-risk-prediction
#5
Jaekwon Kim, Ungu Kang, Youngho Lee
OBJECTIVES: Cardiovascular predictions are related to patients' quality of life and health. Therefore, a risk prediction model for cardiovascular conditions is needed. METHODS: In this paper, we propose a cardiovascular disease prediction model using the sixth Korea National Health and Nutrition Examination Survey (KNHANES-VI) 2013 dataset to analyze cardiovascular-related health data. First, statistical analysis was performed to find variables related to cardiovascular disease using health data related to cardiovascular disease...
July 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/28737705/emotion-recognition-from-chinese-speech-for-smart-affective-services-using-a-combination-of-svm-and-dbn
#6
Lianzhang Zhu, Leiming Chen, Dehai Zhao, Jiehan Zhou, Weishan Zhang
Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emotion classification methods. Five types of features are extracted from a speech sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate and short-term energy...
July 24, 2017: Sensors
https://www.readbyqxmd.com/read/28727565/danoc-an-efficient-algorithm-and-hardware-codesign-of-deep-neural-networks-on-chip
#7
Xichuan Zhou, Shengli Li, Fang Tang, Shengdong Hu, Zhi Lin, Lei Zhang
Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections...
July 18, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28715343/deep-belief-networks-for-electroencephalography-a-review-of-recent-contributions-and-future-outlooks
#8
Faezeh Movahedi, James L Coyle, Ervin Sejdic
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state of- the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28708556/deep-learning-on-sparse-manifolds-for-faster-object-segmentation
#9
Jacinto C Nascimento, Gustavo Carneiro
We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into a rigid detection followed by a non-rigid segmentation, where the low dimensionality of the rigid detection allows for a robust training (i...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28677638/unsupervised-fault-diagnosis-of-a-gear-transmission-chain-using-a-deep-belief-network
#10
Jun He, Shixi Yang, Chunbiao Gan
Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others...
July 4, 2017: Sensors
https://www.readbyqxmd.com/read/28606870/a-deep-learning-approach-for-predicting-the-quality-of-online-health-expert-question-answering-services
#11
Ze Hu, Zhan Zhang, Haiqin Yang, Qing Chen, Decheng Zuo
Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: (1) physicians' answers in an HQA system are usually written in short text, which yields the data sparsity issue; (2) HQA systems apply the quality control mechanism, which refrains the wisdom of crowd...
July 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28562217/deep-restricted-kernel-machines-using-conjugate-feature-duality
#12
Johan A K Suykens
The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal components analysis (PCA), matrix singular value decomposition, and Parzen-type models...
May 31, 2017: Neural Computation
https://www.readbyqxmd.com/read/28502383/rolling-bearing-fault-diagnosis-using-adaptive-deep-belief-network-with-dual-tree-complex-wavelet-packet
#13
Haidong Shao, Hongkai Jiang, Fuan Wang, Yanan Wang
Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT...
May 11, 2017: ISA Transactions
https://www.readbyqxmd.com/read/28486479/multi-label-spacecraft-electrical-signal-classification-method-based-on-dbn-and-random-forest
#14
Ke Li, Nan Yu, Pengfei Li, Shimin Song, Yalei Wu, Yang Li, Meng Liu
In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied...
2017: PloS One
https://www.readbyqxmd.com/read/28473055/automatic-feature-learning-using-multichannel-roi-based-on-deep-structured-algorithms-for-computerized-lung-cancer-diagnosis
#15
Wenqing Sun, Bin Zheng, Wei Qian
This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images...
April 13, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28356908/random-deep-belief-networks-for-recognizing-emotions-from-speech-signals
#16
REVIEW
Guihua Wen, Huihui Li, Jubing Huang, Danyang Li, Eryang Xun
Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28326009/improving-eeg-based-driver-fatigue-classification-using-sparse-deep-belief-networks
#17
Rifai Chai, Sai Ho Ling, Phyo Phyo San, Ganesh R Naik, Tuan N Nguyen, Yvonne Tran, Ashley Craig, Hung T Nguyen
This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28297857/mean-field-message-passing-equations-in-the-hopfield-model-and-its-generalizations
#18
Marc M├ęzard
Motivated by recent progress in using restricted Boltzmann machines as preprocessing algorithms for deep neural network, we revisit the mean-field equations [belief-propagation and Thouless-Anderson Palmer (TAP) equations] in the best understood of such machines, namely the Hopfield model of neural networks, and we explicit how they can be used as iterative message-passing algorithms, providing a fast method to compute the local polarizations of neurons. In the "retrieval phase", where neurons polarize in the direction of one memorized pattern, we point out a major difference between the belief propagation and TAP equations: The set of belief propagation equations depends on the pattern which is retrieved, while one can use a unique set of TAP equations...
February 2017: Physical Review. E
https://www.readbyqxmd.com/read/28269713/an-adaptive-deep-learning-approach-for-ppg-based-identification
#19
V Jindal, J Birjandtalab, M Baran Pouyan, M Nourani
Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269053/deep-learning-framework-for-detection-of-hypoglycemic-episodes-in-children-with-type-1-diabetes
#20
Phyo Phyo San, Sai Ho Ling, Hung T Nguyen
Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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