32 citations · 34 across the 7 of their papers we have counts for
7 papers
From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Data
Yitong Li, Tom Nuno Wolf, Sebastian Pölsterl +3
Differential diagnosis of dementia is challenging due to overlapping symptoms, with structural magnetic resonance imaging (MRI) being the primary method for diagnosis. Despite the…
Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease
Tom Nuno Wolf, Sebastian Pölsterl, Christian Wachinger
Alzheimer's disease (AD) has a complex and multifactorial etiology, which requires integrating information about neuroanatomy, genetics, and cerebrospinal fluid biomarkers for accu…
Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs
Marla Narazani, Ignacio Sarasua, Sebastian Pölsterl +3
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 comp…
CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis
Ignacio Sarasua, Sebastian Pölsterl, Christian Wachinger
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 hetero…
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks
Fabian Bongratz, Anne-Marie Rickmann, Sebastian Pölsterl +1
The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology. Althou…
TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes
Ignacio Sarasua, Sebastian Pölsterl, Christian Wachinger
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 TransforM…