3 papers
cs.CV2025
Learning Disease State from Noisy Ordinal Disease Progression Labels
Gustav Schmidt, Holger Heidrich, Philipp Berens +1
Learning from noisy ordinal labels is a key challenge in medical imaging. In this work, we ask whether ordinal disease progression labels (better, worse, or stable) can be used to…
cs.CV2025
Disentangling representations of retinal images with generative models
Sarah Müller, Lisa M. Koch, Hendrik P. A. Lensch +1
Retinal fundus images play a crucial role in the early detection of eye diseases. However, the impact of technical factors on these images can pose challenges for reliable AI appli…
cs.CV2024
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging
Sarah Müller, Louisa Fay, Lisa M. Koch +3
Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more. As a result, deep learning models tend to l…