no code implementations • 27 Feb 2024 • Fabian Bongratz, Jan Fecht, Anne-Marie Rickmann, Christian Wachinger
In contrast to existing methods, V2C-Long surfaces are directly comparable in a cross-sectional and longitudinal manner.
1 code implementation • 23 Jan 2024 • 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).
1 code implementation • 15 Dec 2023 • Tom Nuno Wolf, Fabian Bongratz, Anne-Marie Rickmann, Sebastian Pölsterl, Christian Wachinger
During inference, similarities of latent features to prototypes are linearly classified to form predictions and attribution maps are provided to explain the similarity.
1 code implementation • 27 Jun 2023 • Fabian Bongratz, Anne-Marie Rickmann, Christian Wachinger
However, the generalization of these deep learning-based approaches to different organs and datasets, a crucial property for deployment in clinical environments, has not yet been assessed.
1 code implementation • 14 Mar 2023 • Anne-Marie Rickmann, Murong Xu, Tom Nuno Wolf, Oksana Kovalenko, Christian Wachinger
The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications.
no code implementations • 19 Sep 2022 • Anne-Marie Rickmann, Fabian Bongratz, Sebastian Pölsterl, Ignacio Sarasua, Christian Wachinger
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD).
1 code implementation • CVPR 2022 • Fabian Bongratz, Anne-Marie Rickmann, Sebastian Pölsterl, Christian Wachinger
The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology.
no code implementations • 23 Apr 2021 • Fabian Gröger, Anne-Marie Rickmann, Christian Wachinger
We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty.
2 code implementations • 30 Apr 2020 • Sinan Özgür Özgün, Anne-Marie Rickmann, Abhijit Guha Roy, Christian Wachinger
The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications.
1 code implementation • 25 Feb 2020 • Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Christian Wachinger
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.
2 code implementations • 11 Jun 2019 • Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Nassir Navab, Christian Wachinger
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.