keyword
MENU ▼
Read by QxMD icon Read
search

Deep learning

keyword
https://www.readbyqxmd.com/read/28214577/parafascicular-thalamic-nucleus-deep-brain-stimulation-decreases-nmda-receptor-glun1-subunit-gene-expression-in-the-prefrontal-cortex
#1
Mónica R Fernández-Cabrera, Abraham Selvas, Miguel Miguéns, Alejandro Higuera-Matas, Anna Vale-Martínez, Emilio Ambrosio, Margarita Martí-Nicolovius, Gemma Guillazo-Blanch
The rodent parafascicular nucleus (PFn) or the centromedian-parafascicular complex of primates is a posterior intralaminar nucleus of the thalamus related to cortical activation and maintenance of states of consciousness underlying attention, learning and memory. Deep brain stimulation (DBS) of the PFn has been proved to restore arousal and consciousness in humans and to enhance performance in learning and memory tasks in rats. The primary expected effect of PFn DBS is to induce plastic changes in target neurons of brain areas associated with cognitive function...
February 15, 2017: Neuroscience
https://www.readbyqxmd.com/read/28212138/deep-learning-in-mammography-diagnostic-accuracy-of-a-multipurpose-image-analysis-software-in-the-detection-of-breast-cancer
#2
Anton S Becker, Magda Marcon, Soleen Ghafoor, Moritz C Wurnig, Thomas Frauenfelder, Andreas Boss
OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set. MATERIALS AND METHODS: In this retrospective, Health Insurance Portability and Accountability Act-compliant study, all patients undergoing mammography in 2012 at our institution were reviewed (n = 3228). All of their prior and follow-up mammographies from a time span of 7 years (2008-2015) were considered as a reference for clinical diagnosis...
February 16, 2017: Investigative Radiology
https://www.readbyqxmd.com/read/28212054/machine-learning-for-medical-imaging
#3
Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L Kline
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region...
February 17, 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/28211015/fifty-years-of-computer-analysis-in-chest-imaging-rule-based-machine-learning-deep-learning
#4
REVIEW
Bram van Ginneken
Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning...
February 16, 2017: Radiological Physics and Technology
https://www.readbyqxmd.com/read/28208671/a-passive-learning-sensor-architecture-for-multimodal-image-labeling-an-application-for-social-robots
#5
Marco A Gutiérrez, Luis J Manso, Harit Pandya, Pedro Núñez
Object detection and classification have countless applications in human-robot interacting systems. It is a necessary skill for autonomous robots that perform tasks in household scenarios. Despite the great advances in deep learning and computer vision, social robots performing non-trivial tasks usually spend most of their time finding and modeling objects. Working in real scenarios means dealing with constant environment changes and relatively low-quality sensor data due to the distance at which objects are often found...
February 11, 2017: Sensors
https://www.readbyqxmd.com/read/28207407/deep-pain-exploiting-long-short-term-memory-networks-for-facial-expression-classification
#6
Pau Rodriguez, Guillem Cucurull, Jordi Gonalez, Josep M Gonfaus, Kamal Nasrollahi, Thomas B Moeslund, F Xavier Roca
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data...
February 9, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28207396/hd-mtl-hierarchical-deep-multi-task-learning-for-large-scale-visual-recognition
#7
Jianping Fan, Tianyi Zhao, Zhenzhong Kuang, Yu Zheng, Ji Zhang, Jun Yu, Jinye Peng
In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically...
February 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28207384/supervised-learning-of-semantics-preserving-hash-via-deep-convolutional-neural-networks
#8
Huei-Fang Yang, Kevin Lin, Chu-Song Chen
This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties...
February 9, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28207383/person-re-identification-by-camera-correlation-aware-feature-augmentation
#9
Ying-Cong Chen, Xiatian Zhu, Wei-Shi Zheng, Jian-Huang Lai
The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i...
February 9, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28205307/segmentation-of-organs-at-risks-in-head-and-neck-ct-images-using-convolutional-neural-networks
#10
Bulat Ibragimov, Lei Xing
PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability. METHODS: Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image...
February 2017: Medical Physics
https://www.readbyqxmd.com/read/28205298/automatic-segmentation-of-the-right-ventricle-from-cardiac-mri-using-a-learning-based-approach
#11
Michael R Avendi, Arash Kheradvar, Hamid Jafarkhani
PURPOSE: This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method. METHODS: The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects)...
February 16, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/28205108/water-assisted-colonoscopy
#12
REVIEW
Sergio Cadoni, Felix W Leung
The current review will attempt to describe the important lessons learned from published randomized controlled trials (RCT) comparing water immersion (WI) or water exchange (WE) techniques with gas insufflation colonoscopy. Air insufflation (AI) to distend the colon to permit visualization and passage through the lumen was developed for diagnostic colonoscopy. When screening colonoscopy was adopted, the same AI method was used. Interval cancers, diagnosed within 3 to 5 years after an index screening colonoscopy, appeared to be linked to low adenoma detection rate (ADR)...
February 15, 2017: Current Treatment Options in Gastroenterology
https://www.readbyqxmd.com/read/28202961/early-brain-development-in-infants-at-high-risk-for-autism-spectrum-disorder
#13
Heather Cody Hazlett, Hongbin Gu, Brent C Munsell, Sun Hyung Kim, Martin Styner, Jason J Wolff, Jed T Elison, Meghan R Swanson, Hongtu Zhu, Kelly N Botteron, D Louis Collins, John N Constantino, Stephen R Dager, Annette M Estes, Alan C Evans, Vladimir S Fonov, Guido Gerig, Penelope Kostopoulos, Robert C McKinstry, Juhi Pandey, Sarah Paterson, John R Pruett, Robert T Schultz, Dennis W Shaw, Lonnie Zwaigenbaum, Joseph Piven
Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life...
February 15, 2017: Nature
https://www.readbyqxmd.com/read/28198674/sequence-specific-bias-correction-for-rna-seq-data-using-recurrent-neural-networks
#14
Yao-Zhong Zhang, Rui Yamaguchi, Seiya Imoto, Satoru Miyano
BACKGROUND: The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures...
January 25, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28192639/a-deep-learning-based-strategy-for-identifying-and-associating-mitotic-activity-with-gene-expression-derived-risk-categories-in-estrogen-receptor-positive-breast-cancers
#15
David Romo-Bucheli, Andrew Janowczyk, Hannah Gilmore, Eduardo Romero, Anant Madabhushi
The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distinguish between the more and less aggressive breast tumors, which is a fundamental criterion for the selection of an appropriate treatment plan, Oncotype DX (ODX) and other gene expression tests are typically employed. While informative, these gene expression tests are expensive, tissue destructive, and require specialized facilities...
February 13, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/28192624/mr-based-synthetic-ct-generation-using-a-deep-convolutional-neural-network-method
#16
Xiao Han
PURPOSE: Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for the purpose of dose calculation and DRR-based patient positioning...
February 13, 2017: Medical Physics
https://www.readbyqxmd.com/read/28191461/three-class-mammogram-classification-based-on-descriptive-cnn-features
#17
M Mohsin Jadoon, Qianni Zhang, Ihsan Ul Haq, Sharjeel Butt, Adeel Jadoon
In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE)...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28190371/occupational-radiation-exposure-of-anesthesia-providers
#18
Rachel R Wang, Amanda H Kumar, Pedro Tanaka, Alex Macario
Anesthesia providers are frequently exposed to radiation during routine patient care in the operating room and remote anesthetizing locations. Eighty-two percent of anesthesiology residents (n = 57 responders) at our institution had a "high" or "very high" concern about the level of ionizing radiation exposure, and 94% indicated interest in educational materials about radiation safety. This article highlights key learning points related to basic physical principles, effects of ionizing radiation, radiation exposure measurement, occupational dose limits, considerations during pregnancy, sources of exposure, factors affecting occupational exposure such as positioning and shielding, and monitoring...
February 1, 2017: Seminars in Cardiothoracic and Vascular Anesthesia
https://www.readbyqxmd.com/read/28188200/stop-think-a-simple-approach-to-encourage-the-self-assessment-of-learning
#19
Richard Guy, Bruce Byrne, Marian Dobos
A simple "stop think" approach was developed to encourage the self-assessment of learning. A key element was the requirement for students to rate their feeling of difficulty before [FOD(pre)] and after [FOD(post)] completing each of three authentic anatomy and physiology concept map exercises. The cohort was divided into low- (group L) and high-performing (group H) groups (based on final subject marks). Both FOD(pre) (group L) and FOD(post) (groups L and H) were significantly negatively correlated with score for some maps...
March 1, 2017: Advances in Physiology Education
https://www.readbyqxmd.com/read/28186895/nonlinear-deep-kernel-learning-for-image-annotation
#20
Mingyuan Jiu, Hichem Sahbi
Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks...
February 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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"