Unrealistic Data Augmentation Improves the Robustness of Deep Learning–Based Classification of Dopamine Transporter SPECT Against Variability Between Sites and Between Cameras

We propose strongly unrealistic data augmentation to improve the robustness of convolutional neural networks (CNNs) for automatic classification of dopamine transporter SPECT against the variability between sites and between cameras.