Improved Motor Outcome Prediction in Parkinson’s Disease Applying Deep Learning to DaTscan SPECT Images
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Title
Improved Motor Outcome Prediction in Parkinson’s Disease Applying Deep Learning to DaTscan SPECT Images
Authors
Keywords
Parkinson's disease, Motor outcome prediction, DAT SPECT, Convolutional neural network
Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume -, Issue -, Pages 104312
Publisher
Elsevier BV
Online
2021-03-07
DOI
10.1016/j.compbiomed.2021.104312
References
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- (2019) Mohammad R. Salmanpour et al. COMPUTERS IN BIOLOGY AND MEDICINE
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- European multicentre database of healthy controls for [123I]FP-CIT SPECT (ENC-DAT): age-related effects, gender differences and evaluation of different methods of analysis
- (2012) Andrea Varrone et al. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
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