THE USE OF GENERATIVE ADVERSARIAL NETWORK AND GRAPH CONVOLUTION NETWORK FOR NEUROIMAGING-BASED DIAGNOSTIC CLASSIFICATION

The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification

Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes.However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out

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Restless Legs Syndrome: The under-recognised condition

Up to Toiletry Bags 15% of the adult population experiences restless legs or Ekbom syndrome during their life [1].The patients may present to a wide range of medical specialities such as general practitioners, general physicians, nephrologists, haematologists, Crimpers obstetricians, endocrinologists, rheumatologists, and neurologists.Around 3% of

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Simulation of Material Influences in MR Tomography

Materials with different magnetic susceptibility can cause deformation of magnetic field in MR tomograph, resulting in errors in obtained image.Using simulation and experimental verification we can solve the effect of changes in homogeneity of static magnetic fields caused RESTORE by specimen Toiletry Bags made from magnetic material.This paper des

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