no code implementations • 7 Feb 2024 • Zhenshan Xie, Simon Dahan, Logan Z. J. Williams, M. Jorge Cardoso, Emma C. Robinson
Cortical surface analysis has gained increased prominence, given its potential implications for neurological and developmental disorders.
1 code implementation • 21 Nov 2023 • Mohamed A. Suliman, Logan Z. J. Williams, Abdulah Fawaz, Emma C. Robinson
Subsequently, features are registered in a deep-discrete manner to optimize the overlap of common structures across surfaces by learning displacements of a set of control points.
1 code implementation • 2 Jun 2023 • Hannah Spitzer, Mathilde Ripart, Abdulah Fawaz, Logan Z. J. Williams, MELD project, Emma Robinson, Juan Eugenio Iglesias, Sophie Adler, Konrad Wagstyl
On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0. 74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0. 64).
1 code implementation • 21 Mar 2023 • Simon Dahan, Abdulah Fawaz, Mohamed A. Suliman, Mariana da Silva, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson
Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis.
1 code implementation • 31 May 2022 • Simon Dahan, Logan Z. J. Williams, Abdulah Fawaz, Daniel Rueckert, Emma C. Robinson
The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds.
1 code implementation • 7 Apr 2022 • Simon Dahan, Hao Xu, Logan Z. J. Williams, Abdulah Fawaz, Chunhui Yang, Timothy S. Coalson, Michelle C. Williams, David E. Newby, A. David Edwards, Matthew F. Glasser, Alistair A. Young, Daniel Rueckert, Emma C. Robinson
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.
1 code implementation • 30 Mar 2022 • Simon Dahan, Abdulah Fawaz, Logan Z. J. Williams, Chunhui Yang, Timothy S. Coalson, Matthew F. Glasser, A. David Edwards, Daniel Rueckert, Emma C. Robinson
Motivated by the success of attention-modelling in computer vision, we translate convolution-free vision transformer approaches to surface data, to introduce a domain-agnostic architecture to study any surface data projected onto a spherical manifold.
no code implementations • 24 Mar 2022 • Mohamed A. Suliman, Logan Z. J. Williams, Abdulah Fawaz, Emma C. Robinson
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.
1 code implementation • 7 Sep 2021 • Simon Dahan, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson
Results show a prediction accuracy of 94. 4% for sex classification (an increase of 6. 2% compared to other methods), and an improvement of correlation with fluid intelligence of 0. 325 vs 0. 144, relative to a baseline model that encodes space and time separately.
1 code implementation • 3 Mar 2021 • Cher Bass, Mariana da Silva, Carole Sudre, Logan Z. J. Williams, Petru-Daniel Tudosiu, Fidel Alfaro-Almagro, Sean P. Fitzgibbon, Matthew F. Glasser, Stephen M. Smith, Emma C. Robinson
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance.