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Found 6 papers, 4 papers with code
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.
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.
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.
Biomechanical modelling of brain atrophy through deep learning
We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations.
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.
