no code implementations • 21 Mar 2022 • Tobias Czempiel, Coco Rogers, Matthias Keicher, Magdalini Paschali, Rickmer Braren, Egon Burian, Marcus Makowski, Nassir Navab, Thomas Wendler, Seong Tae Kim
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
no code implementations • 3 Oct 2021 • Michelle Xiao-Lin Foo, Seong Tae Kim, Magdalini Paschali, Leili Goli, Egon Burian, Marcus Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler
Existing automatic and interactive segmentation models for medical images only use data from a single time point (static).
no code implementations • 29 Jul 2021 • Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni, Magdalini Paschali, Tobias Czempiel, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler
Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.
1 code implementation • 12 Mar 2021 • Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.