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https://www.readbyqxmd.com/read/29150140/visual-pathways-from-the-perspective-of-cost-functions-and-multi-task-deep-neural-networks
#1
REVIEW
H Steven Scholte, Max M Losch, Kandan Ramakrishnan, Edward H F de Haan, Sander M Bohte
Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action...
October 7, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/29147518/machine-learning-molecular-dynamics-for-the-simulation-of-infrared-spectra
#2
Michael Gastegger, Jörg Behler, Philipp Marquetand
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated...
October 1, 2017: Chemical Science
https://www.readbyqxmd.com/read/29147147/collective-behavior-of-large-scale-neural-networks-with-gpu-acceleration
#3
Jingyi Qu, Rubin Wang
In this paper, the collective behaviors of a small-world neuronal network motivated by the anatomy of a mammalian cortex based on both Izhikevich model and Rulkov model are studied. The Izhikevich model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Rulkov model is in the form of difference equations that generate a sequence of membrane potential samples in discrete moments of time to improve computational efficiency. These two models are suitable for the construction of large scale neural networks...
December 2017: Cognitive Neurodynamics
https://www.readbyqxmd.com/read/29145492/3d-multi-view-convolutional-neural-networks-for-lung-nodule-classification
#4
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/29143795/deep-see-joint-object-detection-tracking-and-recognition-with-application-to-visually-impaired-navigational-assistance
#5
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/29143395/a-novel-classification-scheme-to-decline-the-mortality-rate-among-women-due-to-breast-tumor
#6
Bushra Mughal, Muhammad Sharif, Nazeer Muhammad, Tanzila Saba
Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer-aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process...
November 16, 2017: Microscopy Research and Technique
https://www.readbyqxmd.com/read/29136608/variation-of-the-korotkoff-stethoscope-sounds-during-blood-pressure-measurement-analysis-using-a-convolutional-neural-network
#7
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
#8
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/29134430/modern-drug-design-the-implication-of-using-artificial-neuronal-networks-and-multiple-molecular-dynamic-simulations
#9
Oleksandr Yakovenko, Steven J M Jones
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein...
November 13, 2017: Journal of Computer-aided Molecular Design
https://www.readbyqxmd.com/read/29133440/coarse-graining-as-a-downward-causation-mechanism
#10
REVIEW
Jessica C Flack
Downward causation is the controversial idea that 'higher' levels of organization can causally influence behaviour at 'lower' levels of organization. Here I propose that we can gain traction on downward causation by being operational and examining how adaptive systems identify regularities in evolutionary or learning time and use these regularities to guide behaviour. I suggest that in many adaptive systems components collectively compute their macroscopic worlds through coarse-graining. I further suggest we move from simple feedback to downward causation when components tune behaviour in response to estimates of collectively computed macroscopic properties...
December 28, 2017: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
https://www.readbyqxmd.com/read/29131760/deep-learning-a-primer-for-radiologists
#11
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/29130779/experimentally-constrained-circuit-model-of-cortical-arteriole-networks-for-understanding-flow-redistribution-due-to-occlusion-and-neural-activation
#12
Tejapratap Bollu, Nathan R Cornelius, John Sunwoo, Nozomi Nishimura, Chris B Schaffer, Peter C Doerschuk
Computations are described which estimate flows in all branches of the cortical surface arteriole network from two-photon excited fluorescence (2PEF) microscopy images which provide the network topology and, in selected branches red blood cell (RBC) speeds and lumen diameters. Validation is done by comparing the flow predicted by the model with experimentally measured flows and by comparing the predicted flow redistribution in the network due to single-vessel strokes with experimental observations. The model predicts that tissue is protected from RBC flow decreases caused by multiple occlusions of surface arterioles but not penetrating arterioles...
January 1, 2017: Journal of Cerebral Blood Flow and Metabolism
https://www.readbyqxmd.com/read/29128889/the-role-of-neuron-glia-interactions-in-the-emergence-of-ultra-slow-oscillations
#13
Siow-Cheng Chan, Siew-Ying Mok, Danny Wee-Kiat Ng, Sing-Yau Goh
Ultra-slow cortical oscillatory activity of 1-100 mHz has been recorded in human by electroencephalography and in dissociated cultures of cortical rat neurons, but the underlying mechanisms remain to be elucidated. This study presents a computational model of ultra-slow oscillatory activity based on the interaction between neurons and astrocytes. We predict that the frequency of these oscillations closely depends on activation of astrocytes in the network, which is reflected by oscillations of their intracellular calcium concentrations with periods between tens of seconds and minutes...
November 11, 2017: Biological Cybernetics
https://www.readbyqxmd.com/read/29127485/oct-based-deep-learning-algorithm-for-the-evaluation-of-treatment-indication-with-anti-vascular-endothelial-growth-factor-medications
#14
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/29126068/margined-winner-take-all-new-learning-rule-for-pattern-recognition
#15
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/29120633/neural-network-based-prediction-of-conformational-free-energies-a-new-route-towards-coarse-grained-simulation-models
#16
Tobias Lemke, Christine Peter
Coarse-grained (CG) simulation models have become very popular tools to study complex molecular systems with great computational efficiency on length and timescales that are inaccessible to simulations at atomistic resolution. In so-called bottom up coarse-graining strategies, the interactions in the CG model are devised such that an accurate representation of an atomistic sampling of configurational phase space is achieved. This means the coarse-graining methods use the underlying multibody potential of mean force (i...
November 9, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/29118821/tasselnet-counting-maize-tassels-in-the-wild-via-local-counts-regression-network
#17
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/29118806/computer-aided-cobb-measurement-based-on-automatic-detection-of-vertebral-slopes-using-deep-neural-network
#18
Junhua Zhang, Hongjian Li, Liang Lv, Yufeng Zhang
Objective: To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment. Methods: A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs...
2017: International Journal of Biomedical Imaging
https://www.readbyqxmd.com/read/29117910/increased-resting-state-global-functional-connectivity-density-of-default-mode-network-in-schizophrenia-subjects-treated-with-electroconvulsive-therapy
#19
Huan Huang, Yuchao Jiang, Mengqing Xia, Yingying Tang, Tianhong Zhang, Huiru Cui, Junjie Wang, Yu Li, Lihua Xu, Adrian Curtin, Jianhua Sheng, Yuping Jia, Dezhong Yao, Chunbo Li, Cheng Luo, Jijun Wang
Modified electroconvulsive therapy (MECT) has been widely applied to help treat schizophrenia patients who are treatment-resistant to pharmaceutical therapy. Although the technique is increasingly prevalent, the underlying neural mechanisms have not been well clarified. We conducted a longitudinal study to investigate the alteration of global functional connectivity density (gFCD) in schizophrenia patients undergoing MECT using resting state fMRI (functional magnetic resonance imaging). Two groups of schizophrenia inpatients were recruited...
November 6, 2017: Schizophrenia Research
https://www.readbyqxmd.com/read/29117296/polygenic-risk-of-spasmodic-dysphonia-is-associated-with-vulnerable-sensorimotor-connectivity
#20
Gregory Garbès Putzel, Giovanni Battistella, Anna F Rumbach, Laurie J Ozelius, Mert R Sabuncu, Kristina Simonyan
Spasmodic dysphonia (SD), or laryngeal dystonia, is an isolated task-specific dystonia of unknown causes and pathophysiology that selectively affects speech production. Using next-generation whole-exome sequencing in SD patients, we computed polygenic risk score from 1804 genetic markers based on a genome-wide association study in another form of similar task-specific focal dystonia, musician's dystonia. We further examined the associations between the polygenic risk score, resting-state functional connectivity abnormalities within the sensorimotor network, and SD clinical characteristics...
November 19, 2016: Cerebral Cortex
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