AIMC Topic: Amyloid
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PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning.
Clinical nuclear medicine
Mar 1, 2021
PURPOSE: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans.
The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score.
Clinical nuclear medicine
Jun 1, 2020
PURPOSE: The aim of this study was to evaluate random forests (RFs) to identify ROIs on F-florbetapir and F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score.
The Use of Random Forests to Classify Amyloid Brain PET.
Clinical nuclear medicine
Oct 1, 2019
PURPOSE: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification.
Prediction of amyloid aggregation rates by machine learning and feature selection.
The Journal of chemical physics
Aug 28, 2019
A novel data-based machine learning algorithm for predicting amyloid aggregation rates is reported in this paper. Based on a highly nonlinear projection from 16 intrinsic features of a protein and 4 extrinsic features of the environment to the protei...
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