Differential diagnosis of dementia is challenging due to overlapping
symptoms, with structural magnetic resonance imaging (MRI) being the
primary method for diagnosis. Despite the clinical value of computer-
aided differential diagnosis, research has …
Explaining predictions of black-box neural networks is crucial when applied to decision-critical tasks. Thus, attribution maps are commonly used to identify important image regions, despite prior work showing that humans prefer explanations based on …
*Rationale and objectives:*
We evaluate the automatic identification of
type 2 diabetes from neck-to-knee, two-point Dixon MRI scans with 3D
convolutional neural networks on a large, population-based dataset. To
this end, we assess the best …
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 …
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 …