232 citations · 471 across the 17 of their papers we have counts for
3 papers · 1 filter
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni +7
During the first wave of COVID-19, hospitals were overwhelmed with the high number of admitted patients. An accurate prediction of the most likely individual disease progression ca…
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
Tobias Czempiel, Magdalini Paschali, Daniel Ostler +3
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the…
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Seong Tae Kim, Leili Goli, Magdalini Paschali +7
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 a…