1 citations · 1 across the 7 of their papers we have counts for
7 papers
Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework
Christian Gapp, Elias Tappeiner, Martin Welk +8
Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes models to over-rely on domina…
Stacked Ensemble Learning for Abdominal Aortic Aneurysm Segmentation in CT Angiography
Joshua Fry, Sajjad Arzemanzadeh, Saeideh Sekhavat +9
Abdominal aortic aneurysm (AAA) rupture risk assessment increasingly relies on patient-specific biomechanical computations, which require accurate three-dimensional aneurysm geomet…
Multiparameter regularization and aggregation in the context of polynomial functional regression
Elke R. Gizewski, Markus Holzleitner, Lukas Mayer-Suess +2
Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes. In contrast, in this study w…
Sparse2Inverse: Self-supervised inversion of sparse-view CT data
Nadja Gruber, Johannes Schwab, Elke Gizewski +1
Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing. In sparse-view C…
Variational multichannel multiclass segmentation using unsupervised lifting with CNNs
Nadja Gruber, Johannes Schwab, Sebastien Court +2
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks. The variational part is based on a rec…
Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging
Nadja Gruber, Johannes Schwab, Sebastien Court +2
We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties. Our method determin…
Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation
Christoph Angermann, Markus Haltmeier, Ruth Steiger +2
Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge…