  Recent state-of-the-art performances of Vision Transformers (ViT) in computer
vision tasks demonstrate that a general-purpose architecture, which implements
long-range self-attention, could replace the local feature learning operations
of convolutional neural networks. In this paper, we extend ViTs to surfaces by
reformulating the task of surface learning as a sequence-to-sequence learning
problem, by proposing patching mechanisms for general surface meshes. Sequences
of patches are then processed by a transformer encoder and used for
classification or regression. We validate our method on a range of different
biomedical surface domains and tasks: brain age prediction in the developing
Human Connectome Project (dHCP), fluid intelligence prediction in the Human
Connectome Project (HCP), and coronary artery calcium score classification
using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART)
dataset, and investigate the impact of pretraining and data augmentation on
model performance. Results suggest that Surface Vision Transformers (SiT)
demonstrate consistent improvement over geometric deep learning methods for
brain age and fluid intelligence prediction and achieve comparable performance
on calcium score classification to standard metrics used in clinical practice.
Furthermore, analysis of transformer attention maps offers clear and
individualised predictions of the features driving each task. Code is available
on Github: https://github.com/metrics-lab/surface-vision-transformers
