keyword
MENU ▼
Read by QxMD icon Read
search

Deep learning

keyword
https://www.readbyqxmd.com/read/28723578/convolutional-neural-network-based-encoding-and-decoding-of-visual-object-recognition-in-space-and-time
#1
REVIEW
K Seeliger, M Fritsche, U Güçlü, S Schoenmakers, J-M Schoffelen, S E Bosch, M A J van Gerven
Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively...
July 16, 2017: NeuroImage
https://www.readbyqxmd.com/read/28720701/de-novo-peptide-sequencing-by-deep-learning
#2
Ngoc Hieu Tran, Xianglilan Zhang, Lei Xin, Baozhen Shan, Ming Li
De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing...
July 18, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28717579/deep-learning-based-automated-segmentation-of-macular-edema-in-optical-coherence-tomography
#3
Cecilia S Lee, Ariel J Tyring, Nicolaas P Deruyter, Yue Wu, Ariel Rokem, Aaron Y Lee
Evaluation of clinical images is essential for diagnosis in many specialties. Therefore the development of computer vision algorithms to help analyze biomedical images will be important. In ophthalmology, optical coherence tomography (OCT) is critical for managing retinal conditions. We developed a convolutional neural network (CNN) that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians. Using 1,289 OCT images, the CNN segmented images with a 0.911 cross-validated Dice coefficient, compared with segmentations by experts...
July 1, 2017: Biomedical Optics Express
https://www.readbyqxmd.com/read/28715343/deep-belief-networks-for-electroencephalography-a-review-of-recent-contributions-and-future-outlooks
#4
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/28715341/an-automatic-detection-system-of-lung-nodule-based-on-multi-group-patch-based-deep-learning-network
#5
Hongyang Jiang, He Ma, Wei Qian, Mengdi Gao, Yan Li
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography (CT) image transformation, the lung nodule segmentation and the feature extraction, to construct a whole CADe system...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28714187/a-critique-of-utilitarian-and-instrumentalist-concepts-for-the-teaching-of-gross-anatomy-to-medical-and-dental-students-an-opinion-piece
#6
Bernard J Moxham, Diogo Pais
Medical and dental curricula, together with anatomical sciences courses, are increasingly having to change, mainly because there is a drive to being what is termed, without adequate definition, 'clinically relevant'. The concept of 'clinical anatomy' has accordingly been invented and it is expected that, at all times, the teaching of anatomy is directly focussed on clinical scenarios, meaning almost invariably the 'disease-based model of medicine and dentistry'. Furthermore, students are not expected to have a detailed knowledge of gross anatomy and the time devoted to teaching and learning the subject has decreased significantly...
July 16, 2017: Clinical Anatomy
https://www.readbyqxmd.com/read/28713298/linear-relationship-between-resilience-learning-approaches-and-coping-strategies-to-predict-achievement-in-undergraduate-students
#7
Jesús de la Fuente, María Fernández-Cabezas, Matilde Cambil, Manuel M Vera, Maria Carmen González-Torres, Raquel Artuch-Garde
The aim of the present research was to analyze the linear relationship between resilience (meta-motivational variable), learning approaches (meta-cognitive variables), strategies for coping with academic stress (meta-emotional variable) and academic achievement, necessary in the context of university academic stress. A total of 656 students from a southern university in Spain completed different questionnaires: a resiliency scale, a coping strategies scale, and a study process questionnaire. Correlations and structural modeling were used for data analyses...
2017: Frontiers in Psychology
https://www.readbyqxmd.com/read/28713248/deep-brain-magnetic-stimulation-promotes-neurogenesis-and-restores-cholinergic-activity-in-a-transgenic-mouse-model-of-alzheimer-s-disease
#8
Junli Zhen, Yanjing Qian, Jian Fu, Ruijun Su, Haiting An, Wei Wang, Yan Zheng, Xiaomin Wang
Alzheimer's disease (AD) is characterized by progressive decline of memory and cognitive functions. Deep magnetic stimulation (DMS), a noninvasive and nonpharmacological brain stimulation, has been reported to alleviate stress-related cognitive impairment in neuropsychiatric disorders. Our previous study also discovered the preventive effect of DMS on cognitive decline in an AD mouse model. However, the underlying mechanism must be explored further. In this study, we investigated the effect of DMS on spatial learning and memory functions, neurogenesis in the dentate gyrus (DG), as well as expression and activity of the cholinergic system in a transgenic mouse model of AD (5XFAD)...
2017: Frontiers in Neural Circuits
https://www.readbyqxmd.com/read/28712841/long-term-impact-of-subthalamic-stimulation-on-cognitive-function-in-patients-with-advanced-parkinson-s-disease
#9
M Acera, A Molano, B Tijero, G Bilbao, I Lambarri, R Villoria, J Somme, E Ruiz de Gopegui, I Gabilondo, J C Gomez-Esteban
OBJECTIVE: The aim of this study was to evaluate the effects of deep brain stimulation of the subthalamic nucleus (DBS-SN) on cognitive function in patients with Parkinson's disease (PD) 5 years after surgery. MATERIAL AND METHODS: We conducted a prospective study including 50 patients with PD who underwent DBS-SN (62.5% were men; mean age of 62.2±8.2 years; mean progression time of 14.1±6.3 years). All patients were assessed before the procedure and at one year after surgery; 40 patients were further followed up until the 5-year mark...
July 13, 2017: Neurología: Publicación Oficial de la Sociedad Española de Neurología
https://www.readbyqxmd.com/read/28711322/intuitive-physics-current-research-and-controversies
#10
REVIEW
James R Kubricht, Keith J Holyoak, Hongjing Lu
Early research in the field of intuitive physics provided extensive evidence that humans succumb to common misconceptions and biases when predicting, judging, and explaining activity in the physical world. Recent work has demonstrated that, across a diverse range of situations, some biases can be explained by the application of normative physical principles to noisy perceptual inputs. However, it remains unclear how knowledge of physical principles is learned, represented, and applied to novel situations. In this review we discuss theoretical advances from heuristic models to knowledge-based, probabilistic simulation models, as well as recent deep-learning models...
July 12, 2017: Trends in Cognitive Sciences
https://www.readbyqxmd.com/read/28710497/deep-learning-based-radiomics-dlr-and-its-usage-in-noninvasive-idh1-prediction-for-low-grade-glioma
#11
Zeju Li, Yuanyuan Wang, Jinhua Yu, Yi Guo, Wei Cao
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN...
July 14, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28708865/a-deep-learning-framework-for-financial-time-series-using-stacked-autoencoders-and-long-short-term-memory
#12
Wei Bao, Jun Yue, Yulei Rao
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise...
2017: PloS One
https://www.readbyqxmd.com/read/28708557/blind-deep-s3d-image-quality-evaluation-via-local-to-global-feature-aggregation
#13
Heeseok Oh, Sewoong Ahn, Jongyoo Kim, Sanghoon Lee
Previously, no-reference (NR) stereoscopic 3D (S3D) image quality assessment (IQA) algorithms have been limited to the extraction of reliable hand-crafted features based on an understanding of the insufficiently revealed human visual system or natural scene statistics. Furthermore, compared with full-reference (FR) S3D IQA metrics, it is difficult to achieve competitive quality score predictions using the extracted features, which are not optimized with respect to human opinion. To cope with this limitation of the conventional approach, we introduce a novel deep learning scheme for NR S3D IQA in terms of local to global feature aggregation...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708556/deep-learning-on-sparse-manifolds-for-faster-object-segmentation
#14
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/28706534/bag-of-visual-words-model-with-deep-spatial-features-for-geographical-scene-classification
#15
Jiangfan Feng, Yuanyuan Liu, Lin Wu
With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28706185/deep-learning-for-fully-automated-localization-and-segmentation-of-rectal-cancer-on-multiparametric-mr
#16
Stefano Trebeschi, Joost J M van Griethuysen, Doenja M J Lambregts, Max J Lahaye, Chintan Parmer, Frans C H Bakers, Nicky H G M Peters, Regina G H Beets-Tan, Hugo J W L Aerts
Multiparametric Magnetic Resonance Imaging (MRI) can provide detailed information of the physical characteristics of rectum tumours. Several investigations suggest that volumetric analyses on anatomical and functional MRI contain clinically valuable information. However, manual delineation of tumours is a time consuming procedure, as it requires a high level of expertise. Here, we evaluate deep learning methods for automatic localization and segmentation of rectal cancers on multiparametric MR imaging. MRI scans (1...
July 13, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28705497/quicksilver-fast-predictive-image-registration-a-deep-learning-approach
#17
Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer
This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization...
July 10, 2017: NeuroImage
https://www.readbyqxmd.com/read/28699566/entity-recognition-from-clinical-texts-via-recurrent-neural-network
#18
Zengjian Liu, Ming Yang, Xiaolong Wang, Qingcai Chen, Buzhou Tang, Zhe Wang, Hua Xu
BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years...
July 5, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28698781/idiopathic-pulmonary-embolism-in-a-case-of-severe-family-ankrd26-thrombocytopenia
#19
Jerome Guison, Gilles Blaison, Oana Stoica, Remy Hurstel, Marie Favier, Remy Favier
Venous thrombosis affecting thrombocytopenic patients is challenging. We report the case of a woman affected by deep vein thrombosis and pulmonary embolism in a thrombocytopenic context leading to the discovery of a heterozygous mutation in the gene encoding ankyrin repeat domain 26 (ANKRD26) associated with a heterozygous factor V (FV) Leiden mutation. This woman was diagnosed with lower-limb deep vein thrombosis complicated by pulmonary embolism. Severe thrombocytopenia was observed. The genetic study evidenced a heterozygous FV Leiden mutation...
2017: Mediterranean Journal of Hematology and Infectious Diseases
https://www.readbyqxmd.com/read/28695342/thyroid-nodule-classification-in-ultrasound-images-by-fine-tuning-deep-convolutional-neural-network
#20
Jianning Chi, Ekta Walia, Paul Babyn, Jimmy Wang, Gary Groot, Mark Eramian
With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts...
July 10, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
keyword
keyword
50833
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"