367 citations · 696 across the 18 of their papers we have counts for
22 papers · 1 filter
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…
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…
Recalibration of Neural Networks for Point Cloud Analysis
Ignacio Sarasua, Sebastian Poelsterl, Christian Wachinger
Spatial and channel re-calibration have become powerful concepts in computer vision. Their ability to capture long-range dependencies is especially useful for those networks that e…
Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks
Benjamin Gutierrez Becker, Ignacio Sarasua, Christian Wachinger
We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered…
Importance Driven Continual Learning for Segmentation Across Domains
Sinan Özgür Özgün, Anne-Marie Rickmann, Abhijit Guha Roy +1
The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications. However, current neural networks tend…