The current state of computer vision methods applied to autism spectrum disorder (ASD)
research has not been well established. Increasing evidence suggests that computer vision …

Data-driven disease progression models are an emerging set of computational tools that
reconstruct disease timelines for long-term chronic diseases, providing unique insights into …

Self-supervised learning in vision--language processing (VLP) exploits semantic alignment
between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the …

In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical
sensitivity and spatial localisation of group studies, and cortical surface-based alignment …

Implicit representations can provide simple descriptions of relatively complex shapes and
can in many cases be a good choice when designing stable shape processing algorithms …

The Precision Neurology development process implements systems theory with system
biology and neurophysiology in a parallel, bidirectional research path: a combined …

Abstract See Li and Donohue (doi: 10.1093/brain/awy089) for a scientific commentary on
this article. Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to …

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge
compares the performance of algorithms at predicting future evolution of individuals at risk of …

After intense scientific exploration and more than a decade of failed trials, Alzheimer's
disease (AD) remains a fatal global epidemic. A traditional research and drug development …

The emerging field of disease progression modelling provides a natural mechanism to
integrate different kinds of information, for example from imaging, serum and cerebrospinal …