Alzheimer's disease (AD) has a complex and multifactorial etiology,
which requires integrating information about neuroanatomy, genetics,
and cerebrospinal fluid biomarkers for accurate diagnosis. Hence,
recent deep learning approaches combined image …
*Introduction*: Carrying out a randomized controlled trial to estimate
the causal effects of regional brain atrophy due to Alzheimer's
disease (AD) is impossible. Instead, we must estimate causal effects
from observational data. However, this …
The reconstruction of cerebral cortex surfaces from brain MRI scans is
instrumental for the analysis of brain morphology and the detection of
cortical thinning in neurodegenerative diseases like Alzheimer’s
disease (AD). Moreover, for a fine-grained …
Prior work on Alzheimer's Disease (AD) has demonstrated that
convolutional neural networks (CNNs) can leverage the high-dimensional
image information for diagnosing patients. Beside such data-driven
approaches, many established biomarkers exist and …
Alzheimer's Disease (AD) is the most common form of dementia and often
difficult to diagnose due to the multifactorial etiology of dementia.
Recent works on neuroimaging-based computer-aided diagnosis with deep
neural networks (DNNs) showed that …
Modeling temporal changes in subcortical structures is crucial for a
better understanding of the progression of Alzheimer's disease (AD).
Given their flexibility to adapt to heterogeneous sequence lengths,
mesh-based transformer architectures have …
Deep learning offers a powerful approach for analyzing hippocampal changes in
Alzheimer’s disease (AD) without relying on handcrafted features. Nevertheless,
an input format needs to be selected to pass the image information to the neural
network, …
The reconstruction of cortical surfaces from brain magnetic resonance imaging
(MRI) scans is essential for quantitative analyses of cortical thickness and
sulcal morphology. Although traditional and deep learning-based algorithmic
pipelines exist for …
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Diseasediagnosis use different biomarker combinations to classify patients, but do notallow extracting knowledge about the interactions of biomarkers. However, toimprove our …
The longitudinal modeling of neuroanatomical changes related to Alzheimer's disease (AD) is crucial for studying the progression of the disease. To this end,we introduce TransforMesh, a spatio-temporal network based on transformers thatmodels …