1 citations · 1 across the 5 of their papers we have counts for
8 papers
Zero-Shot Brain MRI Inpainting with 2.5D Unconditional Flow Priors
Arnela Hadzic, Franz Thaler, Simon Johannes Joham +1
Generative inpainting of brain MRI volumes is essential for synthesizing healthy tissue in pathological regions, improving the accuracy and reliability of automated downstream brai…
The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification
Olivier Bernard, William A. Romero R., Cyprien Bouton +24
Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Nowadays MI volume quantification…
Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation
Michael Hudler, Franz Thaler, Martin Urschler
Whole-heart multi-compartment CT segmentation is clinically important, but standard CNNs do not explicitly enforce anatomical plausibility. Based on statistics derived from the tra…
Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Franz Thaler, Martin Urschler, Mateusz Kozinski +3
We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation, where we train a network on one domain (e.g., CT) and directly apply i…
Restora-Flow: Mask-Guided Image Restoration with Flow Matching
Arnela Hadzic, Franz Thaler, Lea Bogensperger +2
Flow matching has emerged as a promising generative approach that addresses the lengthy sampling times associated with state-of-the-art diffusion models and enables a more flexible…
Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis
Arnela Hadzic, Simon Johannes Joham, Martin Urschler
Generating synthetic CT (sCT) from MRI or CBCT plays a crucial role in enabling MRI-only and CBCT-based adaptive radiotherapy, improving treatment precision while reducing patient…
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping
Christel Sirocchi, Martin Urschler, Bastian Pfeifer
Interpretable machine learning has emerged as central in leveraging artificial intelligence within high-stakes domains such as healthcare, where understanding the rationale behind…
Federated unsupervised random forest for privacy-preserving patient stratification
Bastian Pfeifer, Christel Sirocchi, Marcus D. Bloice +2
In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques appl…