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https://www.readbyqxmd.com/read/29145492/3d-multi-view-convolutional-neural-networks-for-lung-nodule-classification
#1
Guixia Kang, Kui Liu, Beibei Hou, Ningbo Zhang
The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy...
2017: PloS One
https://www.readbyqxmd.com/read/29144388/hyperspectral-image-enhancement-and-mixture-deep-learning-classification-of-corneal-epithelium-injuries
#2
Siti Salwa Md Noor, Kaleena Michael, Stephen Marshall, Jinchang Ren
In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear...
November 16, 2017: Sensors
https://www.readbyqxmd.com/read/29143795/deep-see-joint-object-detection-tracking-and-recognition-with-application-to-visually-impaired-navigational-assistance
#3
Ruxandra Tapu, Bogdan Mocanu, Titus Zaharia
In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object detection technique designed to localize both static and dynamic objects without any a priori knowledge about their position, type or shape. The methodological core of the proposed approach relies on a novel object tracking method based on two convolutional neural networks trained offline...
October 28, 2017: Sensors
https://www.readbyqxmd.com/read/29142739/classification-of-amyotrophic-lateral-sclerosis-disease-based-on-convolutional-neural-network-and-reinforcement-sample-learning-algorithm
#4
Abdulkadir Sengur, Yaman Akbulut, Yanhui Guo, Varun Bajaj
Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29136608/variation-of-the-korotkoff-stethoscope-sounds-during-blood-pressure-measurement-analysis-using-a-convolutional-neural-network
#5
Fan Pan, Peiyu He, Chengyu Liu, Taiyong Li, Alan Murray, Dingchang Zheng
Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements...
November 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/29135365/deep-learning-to-classify-radiology-free-text-reports
#6
Matthew C Chen, Robyn L Ball, Lingyao Yang, Nathaniel Moradzadeh, Brian E Chapman, David B Larson, Curtis P Langlotz, Timothy J Amrhein, Matthew P Lungren
Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE...
November 13, 2017: Radiology
https://www.readbyqxmd.com/read/29133818/searching-for-prostate-cancer-by-fully-automated-magnetic-resonance-imaging-classification-deep-learning-versus-non-deep-learning
#7
Xinggang Wang, Wei Yang, Jeffrey Weinreb, Juan Han, Qiubai Li, Xiangchuang Kong, Yongluan Yan, Zan Ke, Bo Luo, Tao Liu, Liang Wang
Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH)...
November 13, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29131760/deep-learning-a-primer-for-radiologists
#8
Gabriel Chartrand, Phillip M Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher J Pal, Samuel Kadoury, An Tang
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance...
November 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/29127485/oct-based-deep-learning-algorithm-for-the-evaluation-of-treatment-indication-with-anti-vascular-endothelial-growth-factor-medications
#9
Philipp Prahs, Viola Radeck, Christian Mayer, Yordan Cvetkov, Nadezhda Cvetkova, Horst Helbig, David Märker
PURPOSE: Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications have become the standard of care for their respective indications. Optical coherence tomography (OCT) scans of the central retina provide detailed anatomical data and are widely used by clinicians in the decision-making process of anti-VEGF indication. In recent years, significant progress has been made in artificial intelligence and computer vision research. We trained a deep convolutional artificial neural network to predict treatment indication based on central retinal OCT scans without human intervention...
November 10, 2017: Graefe's Archive for Clinical and Experimental Ophthalmology
https://www.readbyqxmd.com/read/29126070/deep-neural-networks-for-texture-classification-a-theoretical-analysis
#10
Saikat Basu, Supratik Mukhopadhyay, Manohar Karki, Robert DiBiano, Sangram Ganguly, Ramakrishna Nemani, Shreekant Gayaka
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate...
October 23, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29126068/margined-winner-take-all-new-learning-rule-for-pattern-recognition
#11
Kunihiko Fukushima
The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer...
November 7, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29118821/tasselnet-counting-maize-tassels-in-the-wild-via-local-counts-regression-network
#12
Hao Lu, Zhiguo Cao, Yang Xiao, Bohan Zhuang, Chunhua Shen
Background: Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources...
2017: Plant Methods
https://www.readbyqxmd.com/read/29118807/a-novel-active-semisupervised-convolutional-neural-network-algorithm-for-sar-image-recognition
#13
Fei Gao, Zhenyu Yue, Jun Wang, Jinping Sun, Erfu Yang, Huiyu Zhou
Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29114215/feasibility-of-3d-reconstruction-of-neural-morphology-using-expansion-microscopy-and-barcode-guided-agglomeration
#14
Young-Gyu Yoon, Peilun Dai, Jeremy Wohlwend, Jae-Byum Chang, Adam H Marblestone, Edward S Boyden
We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29113103/deep-recurrent-neural-networks-for-human-activity-recognition
#15
Abdulmajid Murad, Jae-Young Pyun
Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data...
November 6, 2017: Sensors
https://www.readbyqxmd.com/read/29109070/artificial-intelligence-learning-semantics-via-external-resources-for-classifying-diagnosis-codes-in-discharge-notes
#16
Chin Lin, Chia-Jung Hsu, Yu-Sheng Lou, Shih-Jen Yeh, Chia-Cheng Lee, Sui-Lung Su, Hsiang-Cheng Chen
BACKGROUND: Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN). OBJECTIVE: Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes...
November 6, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/29097404/deep-learning-of-the-regulatory-grammar-of-yeast-5-untranslated-regions-from-500-000-random-sequences
#17
Josh T Cuperus, Benjamin Groves, Anna Kuchina, Alexander B Rosenberg, Nebojsa Jojic, Stanley Fields, Georg Seelig
Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5' untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5' UTRs and assayed their activity in a massively parallel growth selection experiment...
November 2, 2017: Genome Research
https://www.readbyqxmd.com/read/29095872/multi-categorical-deep-learning-neural-network-to-classify-retinal-images-a-pilot-study-employing-small-database
#18
Joon Yul Choi, Tae Keun Yoo, Jeong Gi Seo, Jiyong Kwak, Terry Taewoong Um, Tyler Hyungtaek Rim
Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture...
2017: PloS One
https://www.readbyqxmd.com/read/29095675/performance-of-a-deep-learning-neural-network-model-in-assessing-skeletal-maturity-on-pediatric-hand-radiographs
#19
David B Larson, Matthew C Chen, Matthew P Lungren, Safwan S Halabi, Nicholas V Stence, Curtis P Langlotz
Purpose To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models. Materials and Methods The institutional review board approved the study. A total of 14 036 clinical hand radiographs and corresponding reports were obtained from two children's hospitals to train and validate the model. For the first test set, composed of 200 examinations, the mean of bone age estimates from the clinical report and three additional human reviewers was used as the reference standard...
November 2, 2017: Radiology
https://www.readbyqxmd.com/read/29089883/resting-state-fmri-functional-connectivity-based-classification-using-a-convolutional-neural-network-architecture
#20
Regina J Meszlényi, Krisztian Buza, Zoltán Vidnyánszky
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups...
2017: Frontiers in Neuroinformatics
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