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Found 9 papers, 6 papers with code
Unsupervised Multimodal Surface Registration with Geometric Deep Learning
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.
Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach
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).
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.
A Deep Generative Model of Neonatal Cortical Surface Development
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 outcomes.
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.
A Deep-Discrete Learning Framework for Spherical Surface Registration
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.
Neural Network Cost Landscapes as Quantum States
We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.
