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…
Separation of target anatomical structure and occlusions in chest radiographs
Johannes Hofmanninger, Sebastian Roehrich, Helmut Prosch +1
Chest radiographs are commonly performed low-cost exams for screening and diagnosis. However, radiographs are 2D representations of 3D structures causing considerable clutter imped…
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
Johannes Hofmanninger, Florian Prayer, Jeanny Pan +3
Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exist, involving sophisti…
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…