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https://www.readbyqxmd.com/read/27922974/training-and-validating-a-deep-convolutional-neural-network-for-computer-aided-detection-and-classification-of-abnormalities-on-frontal-chest-radiographs
#1
Mark Cicero, Alexander Bilbily, Errol Colak, Tim Dowdell, Bruce Gray, Kuhan Perampaladas, Joseph Barfett
OBJECTIVES: Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radiographs, the most frequently performed medical imaging study. We hypothesize CNNs can learn to classify frontal chest radiographs according to common findings from a sufficiently large data set. MATERIALS AND METHODS: Our institution's research ethics board approved a single-center retrospective review of 35,038 adult posterior-anterior chest radiographs and final reports performed between 2005 and 2015 (56% men, average age of 56, patient type: 24% inpatient, 39% outpatient, 37% emergency department) with a waiver for informed consent...
December 5, 2016: Investigative Radiology
https://www.readbyqxmd.com/read/27919482/the-association-of-cognitive-impairment-with-gray-matter-atrophy-and-cortical-lesion-load-in-clinically-isolated-syndrome
#2
Sevda Diker, Arzu Ceylan Has, Aslı Kurne, Rahşan Göçmen, Kader Karlı Oğuz, Rana Karabudak
BACKGROUND: Multiple sclerosis can impair cognition from the early stages and has been shown to be associated with gray matter damage in addition to white matter pathology. OBJECTIVES: To investigate the profile of cognitive impairment in clinically isolated syndrome (CIS), and the contribution of cortical inflammation, cortical and deep gray matter atrophy, and white matter lesions to cognitive decline. METHODS: Thirty patients with clinically isolated syndrome and twenty demographically- matched healthy controls underwent neuropsychologic assessment through the Rao Brief Repeatable Battery, and brain magnetic resonance imaging with double inversion recovery using a 3T scanner...
November 2016: Multiple Sclerosis and related Disorders
https://www.readbyqxmd.com/read/27919220/deepqa-improving-the-estimation-of-single-protein-model-quality-with-deep-belief-networks
#3
Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng
BACKGROUND: Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. RESULTS: We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information...
December 5, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/27917394/a-deep-ensemble-learning-method-for-monaural-speech-separation
#4
Xiao-Lei Zhang, DeLiang Wang
Monaural speech separation is a fundamental problem in robust speech processing. Recently, deep neural network (DNN)-based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have demonstrated remarkable performance improvement. However, a single DNN with a given window length does not leverage contextual information sufficiently, and the differences between the two optimization objectives are not well understood. In this paper, we propose a deep ensemble method, named multicontext networks, to address monaural speech separation...
March 2016: IEEE/ACM Transactions on Audio, Speech, and Language Processing
https://www.readbyqxmd.com/read/27916522/view-tolerant-face-recognition-and-hebbian-learning-imply-mirror-symmetric-neural-tuning-to-head-orientation
#5
Joel Z Leibo, Qianli Liao, Fabio Anselmi, Winrich A Freiwald, Tomaso Poggio
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3-6]...
November 24, 2016: Current Biology: CB
https://www.readbyqxmd.com/read/27916255/mcq-construction-improves-quality-of-essay-assessment-among-undergraduate-dental-students
#6
Peter Yu Tsao Pan, Wendy Wang Chia Wei, Ling Loh Poey, Liang Shen, Victoria Yu Soo Hoon
A well-constructed essay is indicative of deep strategic understanding and is considered a valid assessment tool in many dental schools. It has been suggested that constructing MCQs could be an effective learning tool for students while at the same time contribute towards a pool of well-constructed MCQs that could stand up to scrutiny at high-stakes examinations. This study aimed to compare the quality of essays written by students trained and untrained in MCQ construction. The null hypothesis was that construction of MCQs did not result in higher grades achieved in "closed-book" time-limited assessment conditions...
December 2016: Singapore Dental Journal
https://www.readbyqxmd.com/read/27913366/deepr-a-convolutional-net-for-medical-records
#7
Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh
Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers...
December 1, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27913345/facial-age-estimation-with-age-difference
#8
Zhenzhen Hu, Yonggang Wen, Jianfeng Wang, Meng Wang, Richang Hong, Shuicheng Yan
Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g. the huge amount of human photos in the social networks. These images may provide no age label, but it is easily to derive the age difference for an image pair of the same person...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27911783/deglacial-temperature-history-of-west-antarctica
#9
Kurt M Cuffey, Gary D Clow, Eric J Steig, Christo Buizert, T J Fudge, Michelle Koutnik, Edwin D Waddington, Richard B Alley, Jeffrey P Severinghaus
The most recent glacial to interglacial transition constitutes a remarkable natural experiment for learning how Earth's climate responds to various forcings, including a rise in atmospheric CO2 This transition has left a direct thermal remnant in the polar ice sheets, where the exceptional purity and continual accumulation of ice permit analyses not possible in other settings. For Antarctica, the deglacial warming has previously been constrained only by the water isotopic composition in ice cores, without an absolute thermometric assessment of the isotopes' sensitivity to temperature...
November 28, 2016: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/27911344/patient-perspectives-on-deep-brain-stimulation-clinical-research-in-early-stage-parkinson-s-disease
#10
Lauren Heusinkveld, Mallory Hacker, Maxim Turchan, Madelyn Bollig, Christina Tamargo, William Fisher, Lauren McLaughlin, Adria Martig, David Charles
The FDA approved a multicenter, double-blind, Phase III, pivotal trial testing deep brain stimulation in 280 people with very early stage Parkinson's disease (PD; IDE#G050016). In partnership with The Michael J. Fox Foundation for Parkinson's Research, we conducted a survey to investigate motivating factors, barriers, and gender differences for participation in a trial testing DBS in early PD. The majority of survey respondents (72%) indicated they would consider learning more about participating. Men and women with early PD are likely to consider enrolling in trials of invasive therapies that may slow symptom progression and help future patients...
November 30, 2016: Journal of Parkinson's Disease
https://www.readbyqxmd.com/read/27908154/mass-detection-in-digital-breast-tomosynthesis-deep-convolutional-neural-network-with-transfer-learning-from-mammography
#11
Ravi K Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A Helvie, Jun Wei, Kenny Cha
PURPOSE: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. METHODS: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses)...
December 2016: Medical Physics
https://www.readbyqxmd.com/read/27903077/approaches-to-learning-among-occupational-therapy-undergraduate-students-a-cross-cultural-study
#12
Ted Brown, Kenneth N K Fong, Tore Bonsaksen, Tan Hwei Lan, Yuki Murdolo, Pablo Cruz Gonzalez, Lim Hua Beng
BACKGROUND: Students may adopt various approaches to academic learning. Occupational therapy students' approaches to study and the impact of cultural context have not been formally investigated to date. AIM: To examine the approaches to study adopted by undergraduate occupational therapy students from four different cultural settings. METHOD: 712 undergraduate occupational therapy students (n = 376 from Australia, n = 109 from Hong Kong, n = 160 from Norway and n = 67 from Singapore) completed the Approaches and Study Skills Inventory for Students (ASSIST)...
November 30, 2016: Scandinavian Journal of Occupational Therapy
https://www.readbyqxmd.com/read/27900952/a-novel-deep-learning-approach-for-classification-of-eeg-motor-imagery-signals
#13
Yousef Rezaei Tabar, Ugur Halici
OBJECTIVE: Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. APPROACH: In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals...
November 30, 2016: Journal of Neural Engineering
https://www.readbyqxmd.com/read/27900747/-the-positionality-of-caring-action-small-group-dialogue-in-a-course-on-nursing-ethics
#14
Hsien-Hsien Chiang
BACKGROUND: The content of nursing-ethics education has typically focused on the external standards of caring behavior and neglected the relationship between the ethical attitudes and internal experiences of caregivers. PURPOSE: To explore the embodied experience in order to define the positionality of caring action, which is necessary to enrich the content of nursing ethics through small-group-learning-based dialogue. METHODS: The researcher, as a participant observer, teaches a course on nursing ethics...
December 2016: Hu Li za Zhi the Journal of Nursing
https://www.readbyqxmd.com/read/27898977/artificial-intelligence-with-deep-learning-technology-looks-into-diabetic-retinopathy-screening
#15
Tien Yin Wong, Neil M Bressler
No abstract text is available yet for this article.
November 29, 2016: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/27898976/development-and-validation-of-a-deep-learning-algorithm-for-detection-of-diabetic-retinopathy-in-retinal-fundus-photographs
#16
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C Nelson, Jessica L Mega, Dale R Webster
Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs...
November 29, 2016: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/27898306/dcan-deep-contour-aware-networks-for-object-instance-segmentation-from-histology-images
#17
Hao Chen, Xiaojuan Qi, Lequan Yu, Qi Dou, Jing Qin, Pheng-Ann Heng
In histopathological image analysis, the morphology of histological structures, such as glands and nuclei, has been routinely adopted by pathologists to assess the malignancy degree of adenocarcinomas. Accurate detection and segmentation of these objects of interest from histology images is an essential prerequisite to obtain reliable morphological statistics for quantitative diagnosis. While manual annotation is error-prone, time-consuming and operator-dependant, automated detection and segmentation of objects of interest from histology images can be very challenging due to the large appearance variation, existence of strong mimics, and serious degeneration of histological structures...
November 16, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27898003/human-pose-estimation-from-monocular-images-a-comprehensive-survey
#18
Wenjuan Gong, Xuena Zhang, Jordi Gonzàlez, Andrews Sobral, Thierry Bouwmans, Changhe Tu, El-Hadi Zahzah
Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem...
November 25, 2016: Sensors
https://www.readbyqxmd.com/read/27897236/recognition-of-mould-colony-on-unhulled-paddy-based-on-computer-vision-using-conventional-machine-learning-and-deep-learning-techniques
#19
Ke Sun, Zhengjie Wang, Kang Tu, Shaojin Wang, Leiqing Pan
To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models...
November 29, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27896977/a-deep-learning-approach-for-cancer-detection-and-relevant-gene-identification
#20
Padideh Danaee, Reza Ghaeini, David A Hendrix
Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles...
2016: Pacific Symposium on Biocomputing
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