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20212025
most citedSurface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis

6 citations · 12 across the 13 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

q-bio.NC2022

A Deep Generative Model of Neonatal Cortical Surface Development

Abdulah Fawaz, Logan Z. Williams, A. David Edwards +1

The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outc…

cs.CV2022★ 2 cited

Surface Analysis with Vision Transformers

Simon Dahan, Logan Z. J. Williams, Abdulah Fawaz +2

The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design lim…

eess.IV2022★ 1 cited

Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Simon Dahan, Hao Xu, Logan Z. J. Williams +10

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-attenti…

cs.CV2022★ 6 cited

Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis

Simon Dahan, Abdulah Fawaz, Logan Z. J. Williams +6

The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design lim…

cs.CV2022

A Deep-Discrete Learning Framework for Spherical Surface Registration

Mohamed A. Suliman, Logan Z. J. Williams, Abdulah Fawaz +1

Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches.…