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Learning networks

Rafael Ceschin, Alexandria Zahner, William Reynolds, Jenna Gaesser, Giulio Zuccoli, Cecilia W Lo, Vanathi Gopalakrishnan, Ashok Panigrahy
Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance...
May 21, 2018: NeuroImage
Johannes Uhlig, Annemarie Uhlig, Meike Kunze, Tim Beissbarth, Uwe Fischer, Joachim Lotz, Susanne Wienbeck
OBJECTIVE: The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. SUBJECTS AND METHODS: Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation...
May 24, 2018: AJR. American Journal of Roentgenology
Boyi Hu, Chong Kim, Xiaopeng Ning, Xu Xu
Low back pain remains one of the most prevalent musculoskeletal disorders, while algorithms that able to recognize low back pain patients from healthy population using balance performance data are rarely seen. In this study, human balance and body sway performance during standing trials were utilized to recognize chronic low back pain populations using deep neural networks. To be specific, forty-four chronic LBP and healthy individuals performed static standing tasks while their spine kinematics and center of pressure were recorded...
May 24, 2018: Ergonomics
Jaclyn Bishop, David Cm Kong, Thomas R Schulz, Karin A Thursky, Kirsty L Buising
INTRODUCTION: Antimicrobial resistance (AMR) has been recognised as an urgent health priority, both nationally and internationally. Australian hospitals are required to have an antimicrobial stewardship (AMS) program, yet the necessary resources may not be available in regional, rural or remote hospitals. This review will describe models for AMS programs that have been introduced in regional, rural or remote hospitals internationally and showcase achievements and key considerations that may guide Australian hospitals in establishing or sustaining AMS programs in the regional, rural or remote hospital setting...
May 2018: Rural and Remote Health
Ali Zarezade, Sina Jafarzadeh, Hamid R Rabiee
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited...
2018: PloS One
Xianpeng Li, Ran Liao, Hui Ma, Priscilla T Y Leung, Meng Yan
Polarimetric measurements are becoming increasingly accurate and fast to perform in modern applications. However, analysis on the polarimetric data usually suffers from its high-dimensional nature spatially, temporally, or spectrally. This paper associates polarimetric techniques with metric learning algorithms, namely, polarimetric learning, by introducing a distance metric learning method called Siamese network that aims to learn good distance metrics of algal Mueller matrix images in low-dimensional feature spaces...
May 10, 2018: Applied Optics
Aditya Chattopadhyay, Min Zheng, Mark Paul Waller, U Deva Priyakumar
Knowledge of the structure and dynamics of biomolecules is essential for elucidating the underlying mechanisms of biological processes. Given the stochastic nature of many biological processes, like protein unfolding, it's almost impossible that two independent simulations will generate the exact same sequence of events, which makes direct analysis of simulations difficult. Statistical models like Markov Chains, transition networks etc. help in shedding some light on the mechanistic nature of such processes by predicting long-time dynamics of these systems from short simulations...
May 23, 2018: Journal of Chemical Theory and Computation
Mohammad Rashidi, Robert A Wolkow
Atomic-scale characterization and manipulation with scanning probe microscopy rely upon the use of an atomically sharp probe. Here we present automated methods based on machine learning to automatically detect and recondition the quality of the probe of a scanning tunneling microscope. As a model system, we employ these techniques on the technologically relevant hydrogen-terminated silicon surface, training the network to recognize abnormalities in the appearance of surface dangling bonds. Of the machine learning methods tested, a convolutional neural network yielded the greatest accuracy, achieving a positive identification of degraded tips in 97% of the test cases...
May 23, 2018: ACS Nano
Ornella Valenti, Nace Mikus, Thomas Klausberger
The ability to recognize novel situations is among the most fascinating and vital of the brain functions. A hypothesis posits that encoding of novelty is prompted by failures in expectancy, according to computation matching incoming information with stored events. Thus, unexpected changes in context are detected within the hippocampus and transferred to downstream structures, eliciting the arousal of the dopamine system. Nevertheless, the precise locus of detection is a matter of debate. The dorsal CA1 hippocampus (dCA1) appears as an ideal candidate for operating a mismatch computation and discriminating the occurrence of diverse stimuli within the same environment...
May 22, 2018: Brain Structure & Function
Katrin Starcke, Stephanie Antons, Patrick Trotzke, Matthias Brand
Background and aims Recent research has applied cue-reactivity paradigms to behavioral addictions. The aim of the current meta-analysis is to systematically analyze the effects of learning-based cue-reactivity in behavioral addictions. Methods The current meta-analysis includes 18 studies (29 data sets, 510 participants) that have used a cue-reactivity paradigm in persons with gambling (eight studies), gaming (nine studies), or buying (one study) disorders. We compared subjective, peripheral physiological, electroencephal, and neural responses toward addiction-relevant cues in patients versus control participants and toward addiction-relevant cues versus control cues in patients...
May 23, 2018: Journal of Behavioral Addictions
Philippe Meyer, Vincent Noblet, Christophe Mazzara, Alex Lallement
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning...
May 17, 2018: Computers in Biology and Medicine
Kuan-Hao Su, Jung-Wen Kuo, David W Jordan, Steven Van Hedent, Paul Klahr, Zhouping Wei, Rose Al Helo, Fan Liang, Pengjiang Qian, Gisele C Pereira, Negin Rassouli, Robert C Gilkeson, Bryan J Traughber, Chee-Wai Cheng, Raymond F Muzic
The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z<sub>eff</sub>), relative electron density (ρ<sub>e</sub>), mean excitation energy (<i>I<sub>x</sub></i>), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. 
 Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes...
May 22, 2018: Physics in Medicine and Biology
Ahmed Nait Aicha, Gwenn Englebienne, Kimberley S van Schooten, Mirjam Pijnappels, Ben Kröse
Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk...
May 22, 2018: Sensors
Adil G Khan, Jasper Poort, Angus Chadwick, Antonin Blot, Maneesh Sahani, Thomas D Mrsic-Flogel, Sonja B Hofer
How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR-PV ensembles...
May 21, 2018: Nature Neuroscience
Rashmi Vyas, Anand Zachariah, Isobel Swamidasan, Priya Doris, Ilene Harris
Background: Christian Medical College (CMC), Vellore, India, a tertiary care hospital, designed a year-long Fellowship in Secondary Hospital Medicine (FSHM) for CMC graduates, with the aim to support them during rural service and be motivated to consider practicing in these hospitals. The FSHM was a blend of 15 paper-based distance learning modules, 3 contact sessions, community project work, and networking. This paper reports on the evaluation of the FSHM program. Methods: The curriculum development process for the FSHM reflected the six-step approach including problem identification, needs assessment, formulating objectives, selecting educational strategies, implementation, and evaluation...
September 2017: Education for Health: Change in Training & Practice
Jolene Skordis, Noemi Pace, Marcos Vera-Hernandez, Imran Rasul, Emla Fitzsimons, David Osrin, Dharma Manandhar, Anthony Costello
Models of household decision-making commonly focus on nuclear family members as primary decision-makers. If extended families shape the objectives and constraints of households, then neglecting the role of this network may lead to an incomplete understanding of health-seeking behaviour. Understanding the decision-making processes behind care-seeking may improve behaviour change interventions, better intervention targeting and support health-related development goals. This paper uses data from a cluster randomised trial of a participatory learning and action cycle (PLA) through women's groups, to assess the role of extended family networks as a determinant of gains in health knowledge and health practice...
May 22, 2018: Health Economics, Policy, and Law
Benjamin Davidson, Angelos Kalitzeos, Joseph Carroll, Alfredo Dubra, Sebastien Ourselin, Michel Michaelides, Christos Bergeles
We present a robust deep learning framework for the automatic localisation of cone photoreceptor cells in Adaptive Optics Scanning Light Ophthalmoscope (AOSLO) split-detection images. Monitoring cone photoreceptors with AOSLO imaging grants an excellent view into retinal structure and health, provides new perspectives into well known pathologies, and allows clinicians to monitor the effectiveness of experimental treatments. The MultiDimensional Recurrent Neural Network (MDRNN) approach developed in this paper is the first method capable of reliably and automatically identifying cones in both healthy retinas and retinas afflicted with Stargardt disease...
May 21, 2018: Scientific Reports
Gamal Crichton, Yufan Guo, Sampo Pyysalo, Anna Korhonen
BACKGROUND: Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results...
May 21, 2018: BMC Bioinformatics
Marisa C Ross, Jennifer K Lenow, Clinton D Kilts, Josh M Cisler
Posttraumatic stress disorder (PTSD) is widely associated with deficits in extinguishing learned fear responses, which relies on mechanisms of reinforcement learning (e.g., updating expectations based on prediction errors). However, the degree to which PTSD is associated with impairments in general reinforcement learning (i.e., outside of the context of fear stimuli) remains poorly understood. Here, we investigate brain and behavioral differences in general reinforcement learning between adult women with and without a current diagnosis of PTSD...
May 12, 2018: Journal of Psychiatric Research
Yuan Wang, Yao Wang, Yvonne W Lui
Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals...
May 18, 2018: NeuroImage
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