2 citations · 2 across the 3 of their papers we have counts for
5 papers
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition
Matthias Perkonigg, Johannes Hofmanninger, Georg Langs
Imaging in clinical routine is subject to changing scanner protocols, hardware, or policies in a typically heterogeneous set of acquisition hardware. Accuracy and reliability of de…
Dynamic memory to alleviate catastrophic forgetting in continuous learning settings
Johannes Hofmanninger, Matthias Perkonigg, James A. Brink +3
In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance. Scanner manufacturer, reconstruction kernel, dose, other…
Unsupervised deep clustering for predictive texture pattern discovery in medical images
Matthias Perkonigg, Daniel Sobotka, Ahmed Ba-Ssalamah +1
Predictive marker patterns in imaging data are a means to quantify disease and progression, but their identification is challenging, if the underlying biology is poorly understood.…
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
A. Emre Kavur, N. Sinem Gezer, Mustafa Barış +24
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have intr…
Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma
Roxane Licandro, Johannes Hofmanninger, Matthias Perkonigg +7
The reliable and timely stratification of bone lesion evolution risk in smoldering Multiple Myeloma plays an important role in identifying prime markers of the disease's advance an…