Titlebook: Machine Learning in Clinical Neuroimaging; 4th International Wo Ahmed Abdulkadir,Seyed Mostafa Kia,Thomas Wolfers Conference proceedings 20

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书目名称Machine Learning in Clinical Neuroimaging影响因子(影响力)




书目名称Machine Learning in Clinical Neuroimaging影响因子(影响力)学科排名




书目名称Machine Learning in Clinical Neuroimaging网络公开度




书目名称Machine Learning in Clinical Neuroimaging网络公开度学科排名




书目名称Machine Learning in Clinical Neuroimaging被引频次




书目名称Machine Learning in Clinical Neuroimaging被引频次学科排名




书目名称Machine Learning in Clinical Neuroimaging年度引用




书目名称Machine Learning in Clinical Neuroimaging年度引用学科排名




书目名称Machine Learning in Clinical Neuroimaging读者反馈




书目名称Machine Learning in Clinical Neuroimaging读者反馈学科排名

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Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep eep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer’s Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deforma

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Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian btle tasks such as the identification of barely visible brain lesions, especially given the lack of annotated datasets. Good candidate approaches are patch-based unsupervised pipelines which have both the advantage to increase the number of input data and to capture local and fine anomaly patterns d

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MRI Image Registration Considerably Improves CNN-Based Disease Classificationsonance imaging (MRI) brain scans. These scans usually undergo several preprocessing steps, including image registration. However, the effect of image registration methods on the performance of the machine learning classifier is poorly understood. In this study, we train a convolutional neural netwo

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PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstructione introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each

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