Search Results for author: Logan Z. J. Williams

Found 10 papers, 8 papers with code

Cortical Surface Diffusion Generative Models

no code implementations7 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.

Unsupervised Multimodal Surface Registration with Geometric Deep Learning

1 code implementation21 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.

Image Registration

Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach

1 code implementation2 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).

Lesion Detection Semantic Segmentation +1

The Multiscale Surface Vision Transformer

1 code implementation21 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.

Surface Analysis with Vision Transformers

1 code implementation31 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.

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

1 code implementation7 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.

Classification Data Augmentation

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

1 code implementation30 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.

A Deep-Discrete Learning Framework for Spherical Surface Registration

no code implementations24 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.

Image Registration Multi-Label Classification

Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

1 code implementation7 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.

Action Recognition Skeleton Based Action Recognition

ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

1 code implementation3 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.

Disentanglement Image Registration +1

Cannot find the paper you are looking for? You can Submit a new open access paper.