most citedHow to Choose a Reinforcement-Learning Algorithm

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Template-Based Cortical Surface Reconstruction with Minimal Energy Deformation

Patrick Madlindl, Fabian Bongratz, Christian Wachinger

Cortical surface reconstruction (CSR) from magnetic resonance imaging (MRI) is fundamental to neuroimage analysis, enabling morphological studies of the cerebral cortex and functio…

cs.GR2025

X-SiT: Inherently Interpretable Surface Vision Transformers for Dementia Diagnosis

Fabian Bongratz, Tom Nuno Wolf, Jaume Gual Ramon +1

Interpretable models are crucial for supporting clinical decision-making, driving advances in their development and application for medical images. However, the nature of 3D volume…

cs.CV2024

MLV-Net: Rater-Based Majority-Label Voting for Consistent Meningeal Lymphatic Vessel Segmentation

Fabian Bongratz, Markus Karmann, Adrian Holz +9

Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as w…

cs.LG20241 cited

How to Choose a Reinforcement-Learning Algorithm

Fabian Bongratz, Vladimir Golkov, Lukas Mautner +5

The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an…

eess.IV2024

Neural deformation fields for template-based reconstruction of cortical surfaces from MRI

Fabian Bongratz, Anne-Marie Rickmann, Christian Wachinger

The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the cerebral cortex in magnetic resonance imaging (MRI). Existing segmentation-based methods…