  We present our method for gestational age at birth prediction for the SLCN
(surface learning for clinical neuroimaging) challenge. Our method is based on
a multi-view shape analysis technique that captures 2D renderings of a 3D
object from different viewpoints. We render the brain features on the surface
of the sphere and then the 2D images are analyzed via 2D CNNs and an attention
layer for the regression task. The regression task achieves a MAE of 1.637 +-
1.3 on the Native space and MAE of 1.38 +- 1.14 on the template space. The
source code for this project is available in our github repository
https://github.com/MathieuLeclercq/SLCN_challenge_UNC
