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The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
Jakob Dexl, Katharina Jeblick, Andreas Mittermeier +27
We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generaliza…
MIMM-X: Disentangling Spurious Correlations for Medical Image Analysis
Louisa Fay, Hajer Reguigui, Bin Yang +2
Deep learning models can excel on medical tasks, yet often experience spurious correlations, known as shortcut learning, leading to poor generalization in new environments. Particu…
Retrospective motion correction in MRI using disentangled embeddings
Qi Wang, Veronika Ecker, Marcel Früh +2
Physiological motion can affect the diagnostic quality of magnetic resonance imaging (MRI). While various retrospective motion correction methods exist, many struggle to generalize…
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…