Classification of amyloid status using machine learning with histograms of oriented 3D gradients

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• 作者：Liam Cattell^a ; ^{liam.cattell@eng.ox.ac.uk} ; Gü ; nther Platsch^b ; Richie Pfeiffer^c ; Jé ; rô ; me Declerck^b ; Julia A. Schnabel^d ; Chloe Hutton^b ; for the Alzheimer's Disease Neuroimaging Initiative¹
• 关键词：Amyloid ; Positron emission tomography ; Florbetapir ; Florbetaben ; Pittsburgh compound B ; Classification
• 刊名：NeuroImage: Clinical
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：12
• 期：Complete
• 页码：990-1003
• 全文大小：2509 K
• 卷排序：12

文摘

A machine learning approach to brain amyloid status classification is proposed. The method is independent of PET tracer and requires little recalibration. Classification accuracy was higher than SUVR for three amyloid tracers.