10 citations · 10 across the 5 of their papers we have counts for
5 papers
Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathology
Matthias Perkonigg, Patrick Rockenschaub, Georg Göbel +1
Accurate brain tumor classification is critical for intra-operative decision making in neuro-oncological surgery. However, existing approaches are restricted to a fixed set of pred…
Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training
Daniel Sobotka, Alexander Herold, Matthias Perkonigg +5
Liver vessel segmentation in magnetic resonance imaging data is important for the computational analysis of vascular remodelling, associated with a wide spectrum of diffuse liver d…
Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease
Matthias Perkonigg, Nina Bastati, Ahmed Ba-Ssalamah +6
Quantifiable image patterns associated with disease progression and treatment response are critical tools for guiding individual treatment, and for developing novel therapies. Here…
Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies
Matthias Perkonigg, Peter Mesenbrink, Alexander Goehler +3
In multi-center randomized clinical trials imaging data can be diverse due to acquisition technology or scanning protocols. Models predicting future outcome of patients are impaire…
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Matthias Perkonigg, Johannes Hofmanninger, Christian Herold +2
Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition setti…