232 citations · 573 across the 29 of their papers we have counts for
6 papers · 1 filter
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Michelle Xiao-Lin Foo, Seong Tae Kim, Magdalini Paschali +6
Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic…
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…
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis
Christoph Berger, Magdalini Paschali, Ben Glocker +1
Image classification models deployed in the real world may receive inputs outside the intended data distribution. For critical applications such as clinical decision making, it is…
Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
Maria Tirindelli, Christine Eilers, Walter Simson +3
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the t…
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…