367 citations · 696 across the 20 of their papers we have counts for
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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…
Alzheimer's Disease Diagnosis via Deep Factorization Machine Models
Raphael Ronge, Kwangsik Nho, Christian Wachinger +1
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting kn…
Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform
Sebastian Pölsterl, Tom Nuno Wolf, Christian Wachinger
Prior work on diagnosing Alzheimer's disease from magnetic resonance images of the brain established that convolutional neural networks (CNNs) can leverage the high-dimensional ima…
Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data
Sebastian Pölsterl, Christina Aigner, Christian Wachinger
Deep Neural Networks (DNNs) have an enormous potential to learn from complex biomedical data. In particular, DNNs have been used to seamlessly fuse heterogeneous information from n…
Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer's Disease
Ignacio Sarasua, Jonwong Lee, Christian Wachinger
Geometric deep learning can find representations that are optimal for a given task and therefore improve the performance over pre-defined representations. While current work has ma…
STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains
Fabian Gröger, Anne-Marie Rickmann, Christian Wachinger
We propose an unsupervised domain adaptation (UDA) approach for white matter hyperintensity (WMH) segmentation, which uses Self-Training with Uncertainty DEpendent Label refinement…