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Found 17 papers, 11 papers with code
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait.
ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans
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.
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.
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 Analysis with Vision Transformers
The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds.
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.
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs
Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively.
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.
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI).
Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction
The importance of each SVF, which is estimated by learned attention maps, is conditioned on the age of the neonates and varies with the time step of integration.
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.
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy.
Surface Agnostic Metrics for Cortical Volume Segmentation and Regression
The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders.
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts.
Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks
Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution.
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.
Cortical Surface Diffusion Generative Models
Cortical surface analysis has gained increased prominence, given its potential implications for neurological and developmental disorders.
