  Using 3D CNNs on high resolution medical volumes is very computationally
demanding, especially for large datasets like the UK Biobank which aims to scan
100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D
projections (representing mean and standard deviation across axial, sagittal
and coronal slices) of the 3D volumes leads to reasonable test accuracy when
predicting the age from brain volumes. Using our approach, one training epoch
with 20,324 subjects takes 20 - 50 seconds using a single GPU, which two orders
of magnitude faster compared to a small 3D CNN. These results are important for
researchers who do not have access to expensive GPU hardware for 3D CNNs.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning