Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentati…
2021
Purpose: Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation. Materials and Methods: One-hundred-three patients with median lobe enlargement on prostate MRI were retrospectively included. Ellipsoid formula, manual segmentation and automatic segmentation were used for prostate volume estimation using T2 weighted MRI images. ENet was used for automatic segmentation; it is a deep learning network developed for fast inference and high accuracy in augmented reality and automotive scenarios. Student t-test was performed to compare prostate vol…
Maturation changes the excitability and effective connectivity of the frontal lobe : A developmental TMS-EEG study
2019
The combination of transcranial magnetic stimulation with simultaneous electroencephalography (TMS–EEG) offers direct neurophysiological insight into excitability and connectivity within neural circuits. However, there have been few developmental TMS–EEG studies to date, and they all have focused on primary motor cortex stimulation. In the present study, we used navigated high‐density TMS–EEG to investigate the maturation of the superior frontal cortex (dorsal premotor cortex [PMd]), which is involved in a broad range of motor and cognitive functions known to develop with age. We demonstrated that reactivity to frontal cortex TMS decreases with development. We also showed that although fron…
Built Environment, Psychosocial Factors and Active Commuting to School in Adolescents: Clustering a Self-Organizing Map Analysis
2018
Although the built environment and certain psychosocial factors are related to adolescents&rsquo
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review
2015
Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10years. This survey aims to provide a comprehen…
rTMS evidence of different delay and decision processes in a fronto-parietal neuronal network activated during spatial working memory.
2003
The existence of a specific and widely distributed network for spatial working memory (WM) in humans, involving the posterior parietal cortex and the prefrontal cortex, is supported by a number of neuroimaging studies. We used a repetitive transcranial magnetic stimulation (rTMS) approach to investigate the temporal dynamics and the reciprocal interactions of the different areas of the parieto-frontal network in normal subjects performing a spatial WM task, with the aim to compare neural activity of the different areas in the delay and decision phases of the task. Trains of rTMS at 25 Hz were delivered over the posterior parietal cortex (PPC), the premotor cortex (SFG) and the dorsolateral …
Why retail investors traded equity during the pandemic? An application of artificial neural networks to examine behavioral biases
2021
Behavioral biases are known to influence the investment decisions of retail investors. Indeed, extant research has revealed interesting findings in this regard. However, the literature on the impact of these biases on millennials' trading activity, particularly during a health crisis like the COVID-19 pandemic, as well as the equity recommendation intentions of such investors, is limited. The present study addressed these gaps by investigating the influence of eight behavioral biases: overconfidence and self-attribution, over-optimism, hindsight, representativeness, anchoring, loss aversion, mental accounting, and herding on the trading activity and recommendation intentions of millennials …
Convolutional Neural Networks for Multispectral Image Cloud Masking
2020
Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems. One of their major strengths is that, when enough data is available, CNN perform an end-to-end learning without the need of custom feature extraction methods. In this work, we study the use of different CNN architectures for cloud masking of Proba-V multispectral images. We compare such methods with the more classical machine learning approach based on feature extraction plus supervised classification. Experimental results suggest that CNN are a promising alternative for solving cloud masking problems.
V1 non-linear properties emerge from local-to-global non-linear ICA
2006
It has been argued that the aim of non-linearities in different visual and auditory mechanisms may be to remove the relations between the coefficients of the signal after global linear ICA-like stages. Specifically, in Schwartz and Simoncelli (2001), it was shown that masking effects are reproduced by fitting the parameters of a particular non-linearity in order to remove the dependencies between the energy of wavelet coefficients. In this work, we present a different result that supports the same efficient encoding hypothesis. However, this result is more general because, instead of assuming any specific functional form for the non-linearity, we show that by using an unconstrained approach…
Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification
2021
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution. For 3D medical images, it is often impractical to build a large homogeneous annotated dataset for a specific pathology. Self-supervised methods offer a new way to learn a representation of the images in an unsupervised manner with a neural network. In particular, contrastive learning has shown great promises by (almost) matching the performance of fully-supervised CNN on vision tasks. Nonetheless, this method does not take advantage of available meta-data, such as participant’s age, viewed as prior knowledge. Here, we propose to leverage continuous proxy me…
Functional Differential and Difference Equations with Applications
2012
and Applied Analysis 3 solutions to a class of nonlocal boundary value problems for linear homogeneous secondorder functional differential equations with piecewise constant arguments are obtained. The last but not the least, this issue features a number of publications that report recent progress in the analysis of problems arising in various applications. In particular, dynamics of delayed neural network models consisting of two neurons with inertial coupling were studied, properties of a stochastic delay logistic model under regime switching were explored, and analysis of the permanence and extinction of a single species with contraception and feedback controls was conducted. Other applie…