keyword
MENU ▼
Read by QxMD icon Read
search

Deep learning

keyword
https://www.readbyqxmd.com/read/28107548/mri-based-prostate-cancer-detection-with-high-level-representation-and-hierarchical-classification
#1
Yulian Zhu, Li Wang, Mingxia Liu, Chunjun Qian, Ambereen Yousuf, Aytekin Oto, Dinggang Shen
PURPOSE: Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on high-level feature representation for constructing hierarchical classification to refine the detection results. METHODS: High-level feature representation is first learned by a deep learning network, where multi-parametric MR images are used as the input data. Then, based on the learned high-level features, a hierarchical classification method is developed, where multiple random forest classifiers are iteratively constructed to refine the detection results of prostate cancer...
January 20, 2017: Medical Physics
https://www.readbyqxmd.com/read/28106716/tracking-and-classification-of-in-air-hand-gesture-based-on-thermal-guided-joint-filter
#2
Seongwan Kim, Yuseok Ban, Sangyoun Lee
The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition...
January 17, 2017: Sensors
https://www.readbyqxmd.com/read/28106675/treatment-of-slipped-capital-femoral-epiphysis-with-the-modified-dunn-procedure-a-multicenter-study
#3
Masquijo J Javier, Allende Victoria, D'Elia Martín, Miranda Gabriela, Fernández Claudio A
INTRODUCTION: Treatment of moderate to severe slipped capital femoral epiphysis (SCFE) is controversial. Over the last years, 3 institutions in Argentina adopted the modified Dunn procedure for capital realignment in selected cases of SCFE. Our aim in this study was to evaluate the clinical outcome and the rate of complications of patients who had undergone surgical hip dislocation and capital realignment. METHODS: A multicenter retrospective cohort study of patients who received the modified Dunn procedure from January 2009 to 2013 was performed...
January 18, 2017: Journal of Pediatric Orthopedics
https://www.readbyqxmd.com/read/28105470/bladder-cancer-segmentation-in-ct-for-treatment-response-assessment-application-of-deep-learning-convolution-neural-network-a-pilot-study
#4
COMMENT
Kenny H Cha, Lubomir M Hadjiiski, Ravi K Samala, Heang-Ping Chan, Richard H Cohan, Elaine M Caoili, Chintana Paramagul, Ajjai Alva, Alon Z Weizer
Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases. 65 000 regions of interests were extracted from pre-treatment CT images to train a deep-learning convolution neural network (DL-CNN) for tumor boundary detection using leave-one-case-out cross-validation...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28103558/effective-multi-query-expansions-collaborative-deep-networks-based-feature-learning-for-robust-landmark-retrieval
#5
Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang
Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and/or angles, and may subsequently yield very different results...
January 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28099803/deep-learning-to-predict-the-formation-of-quinone-species-in-drug-metabolism
#6
Tyler B Hughes, S Joshua Swamidass
Many adverse drug reactions are thought to be caused by electrophilically reactive drug metabolites that conjugate to nucleophilic sites within DNA and proteins, causing cancer or toxic immune responses. Quinone species, including quinone-imines, quinone-methides, and imine-methides, are electrophilic Michael acceptors that are of- ten highly reactive, and comprise over 40% of all known reactive metabolites. Quinone metabolites are created by cytochromes P450 and peroxidases. For example, cy- tochromes P450 oxidize acetaminophen to N-acetyl-p-benzoquinone imine, which is electrophilically reactive and covalently binds to nucleophilic sites within proteins...
January 18, 2017: Chemical Research in Toxicology
https://www.readbyqxmd.com/read/28098774/learning-to-diagnose-cirrhosis-with-liver-capsule-guided-ultrasound-image-classification
#7
Xiang Liu, Jia Lin Song, Shuo Hong Wang, Jing Wen Zhao, Yan Qiu Chen
This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images...
January 13, 2017: Sensors
https://www.readbyqxmd.com/read/28095645/what-we-can-learn-from-a-tadpole-about-ciliopathies-and-airway-diseases-using-systems-biology-in-xenopus-to-study-cilia-and-mucociliary-epithelia
#8
REVIEW
Peter Walentek, Ian K Quigley
Over the past years, the Xenopus embryo has emerged as an incredibly useful model organism for studying the formation and function of cilia and ciliated epithelia in vivo. This has led to a variety of findings elucidating the molecular mechanisms of ciliated cell specification, basal body biogenesis, cilia assembly and ciliary motility. These findings also revealed the deep functional conservation of signaling, transcriptional, post-transcriptional and protein networks employed in the formation and function of vertebrate ciliated cells...
January 17, 2017: Genesis: the Journal of Genetics and Development
https://www.readbyqxmd.com/read/28095195/deep-learning-with-dynamic-spiking-neurons-and-fixed-feedback-weights
#9
Arash Samadi, Timothy P Lillicrap, Douglas B Tweed
Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation...
January 17, 2017: Neural Computation
https://www.readbyqxmd.com/read/28094850/discriminating-solitary-cysts-from-soft-tissue-lesions-in-mammography-using-a-pretrained-deep-convolutional-neural-network
#10
Thijs Kooi, Bram van Ginneken, Nico Karssemeijer, Ard den Heeten
PURPOSE: It is estimated that 7% of women in the western world will develop palpable breast cysts in their lifetime. Even though cysts have been correlated with risk of developing breast cancer, many of them are benign and do not require follow-up. We develop a method to discriminate benign solitary cysts from malignant masses in digital mammography. We think a system like this can have merit in the clinic as a decision aid or complementary to specialised modalities. METHODS: We employ a deep Convolutional Neural Network (CNN) to classify cyst and mass patches...
January 17, 2017: Medical Physics
https://www.readbyqxmd.com/read/28094425/learning-curve-in-anatomo-electrophysiological-correlations-in-subthalamic-nucleus-stimulation
#11
Dušan Hrabovsky, Marek Balaz, Martina Bockova, Věra Feitova, Zdeněk Novak, Jan Chrastina
AIM: Advances in neuroradiological planning techniques in deep brain stimulation put the need for intraoperative electrophysiological monitoring into doubt. Moreover intraoperative monitoring prolongs surgical time and there is potential association between the use of microelectrodes and increased incidence of hemorrhagic complications Material and Methods: The study analyzes the correlation between the anatomically planned trajectory and the final subthalamic electrode placement after electrophysiological monitoring in patients with Parkinson's disease and its change with the increasing experience of the surgical team...
December 14, 2016: Turkish Neurosurgery
https://www.readbyqxmd.com/read/28092583/deep-learning-and-insomnia-assisting-clinicians-with-their-diagnosis
#12
Mostafa Shahin, Beena Ahmed, Sana Tmar-Ben Hamida, Fathima Mulaffer, Martin Glos, Thomas Penzel
Effective sleep analysis is hampered by the lack of automated tools catering for disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channels to accurately differentiate between patients with insomnia or controls with no sleep complaints. We investigated two different approaches to achieve this. The first approach used EEG data from the whole sleep recording irrespective of the sleep stage (stage-independent classification), while the second used only EEG data from insomnia-impacted specific sleep stages (stage-dependent classification)...
January 9, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28092555/progressive-shape-distribution-encoder-for-3d-shape-retrieval
#13
Jin Xie, Fan Zhu, Guoxian Dai, Ling Shao, Yi Fang
Since there are complex geometric variations with 3D shapes, extracting efficient 3D shape features is one of the most challenging tasks in shape matching and retrieval. In this paper, we propose a deep shape descriptor by learning shape distributions at different diffusion time via a progressive shape-distribution-encoder (PSDE). First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometry structures of 3D shapes. Then, we propose to learn a deep shape feature through an unsupervised PSDE...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092553/deep-aesthetic-quality-assessment-with-semantic-information
#14
Yueying Kao, Ran He, Kaiqi Huang
Human beings often assess the aesthetic quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic aesthetic quality assessment and semantic recognition. We cast the assessment problem as the main task among a multitask deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of aesthetic and semantic labels...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28087243/using-deep-learning-to-investigate-the-neuroimaging-correlates-of-psychiatric-and-neurological-disorders-methods-and-applications
#15
REVIEW
Sandra Vieira, Walter H L Pinaya, Andrea Mechelli
Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition. DL differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecutive nonlinear transformations, achieving increasingly higher levels of abstraction and complexity. Given its ability to detect abstract and complex patterns, DL has been applied in neuroimaging studies of psychiatric and neurological disorders, which are characterised by subtle and diffuse alterations...
January 10, 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/28079731/midterm-radiographic-and-functional-outcomes-of-the-anterior-internal-pelvic-fixator-infix
#16
Rahul Vaidya, Adam Jonathan Martin, Matthew Roth, Frederick Tonnos, Bryant Oliphant, Jon Carlson
OBJECTIVE: To describe our experience using the anterior internal pelvic fixator (INIFX) for treating pelvic ring injuries. DESIGN: Case Series SETTING:: Level 1 Trauma Center PATIENTS:: Eighty-three patients with pelvic ring injuries treated with INFIX. Follow up avg. 35 months (range 12-80.33) INTERVENTION:: Surgical treatment of pelvic ring injuries included reduction, appropriate posterior fixation, and INFIX placement. OUTCOME MEASUREMENTS: Reduction using the pelvic deformity index (PDI) and pubic symphysis(PS) widening, Majeed functional scores, complications; infection, implant failure, heterotopic ossification (HO), nerve injury, and pain...
January 5, 2017: Journal of Orthopaedic Trauma
https://www.readbyqxmd.com/read/28075373/visual-object-tracking-based-on-cross-modality-gaussian-bernoulli-deep-boltzmann-machines-with-rgb-d-sensors
#17
Mingxin Jiang, Zhigeng Pan, Zhenzhou Tang
Visual object tracking technology is one of the key issues in computer vision. In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D sensors. First, a cross-modality featurelearning network based on aGaussian-Bernoulli DBM is constructed, which can extract cross-modality features of the samples in RGB-D video data. Second, the cross-modality features of the samples are input into the logistic regression classifier, andthe observation likelihood model is established according to the confidence score of the classifier...
January 10, 2017: Sensors
https://www.readbyqxmd.com/read/28070484/deep-learning-predictions-of-survival-based-on-mri-in-amyotrophic-lateral-sclerosis
#18
Hannelore K van der Burgh, Ruben Schmidt, Henk-Jan Westeneng, Marcel A de Reus, Leonard H van den Berg, Martijn P van den Heuvel
Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28068291/particle-swarm-optimization-for-programming-deep-brain-stimulation-arrays
#19
Edgar Peña, Simeng Zhang, Steve Deyo, YiZi Xiao, Matthew D Johnson
OBJECTIVE: Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. APPROACH: Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions...
February 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28067221/quantum-chemical-insights-from-deep-tensor-neural-networks
#20
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, Alexandre Tkatchenko
Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol(-1)) predictions in compositional and configurational chemical space for molecules of intermediate size...
January 9, 2017: Nature Communications
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"