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20222026
most citedHow to Choose a Reinforcement-Learning Algorithm

1 citations · 2 across the 16 of their papers we have counts for

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5 papers · 1 filter

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.LG2024★ 1 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…

cs.CV2024

Individualized Mapping of Aberrant Cortical Thickness via Stochastic Cortical Self-Reconstruction

Christian Wachinger, Dennis Hedderich, Melissa Thalhammer +1

Understanding individual differences in cortical structure is key to advancing diagnostics in neurology and psychiatry. Reference models aid in detecting aberrant cortical thicknes…

eess.IV2024

V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal Correspondence

Fabian Bongratz, Jan Fecht, Anne-Marie Rickmann +1

Reconstructing the cortex from longitudinal magnetic resonance imaging (MRI) is indispensable for analyzing morphological alterations in the human brain. Despite the recent advance…

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…