Search Results for author:
Found 7 papers, 6 papers with code
Cortical Surface Diffusion Generative Models
Cortical surface analysis has gained increased prominence, given its potential implications for neurological and developmental disorders.
Surface Masked AutoEncoder: Self-Supervision for Cortical Imaging Data
By reconstructing surface data from a masked version of the input, the proposed method effectively models cortical structure to learn strong representations that translate to improved performance in downstream tasks.
The Multiscale Surface Vision Transformer
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
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
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.
Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis
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.
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
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.
