4 citations · 6 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…
Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models
Indu Ilanchezian, Valentyn Boreiko, Laura Kühlewein +5
Counterfactual reasoning is often used in clinical settings to explain decisions or weigh alternatives. Therefore, for imaging based specialties such as ophthalmology, it would be…