232 citations · 328 across the 25 of their papers we have counts for
6 papers · 1 filter
CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN
Matan Atad, Vitalii Dmytrenko, Yitong Li +6
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature. To shed light on these black-box models, previous works…
U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images
Marcel Kollovieh, Matthias Keicher, Stephan Wunderlich +3
Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the…
Interpretable Vertebral Fracture Diagnosis
Paul Engstler, Matthias Keicher, David Schinz +11
Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability quenches scientific curiosity but also lea…
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
Tobias Czempiel, Coco Rogers, Matthias Keicher +7
Quantifying COVID-19 infection over time is an important task to manage the hospitalization of patients during a global pandemic. Recently, deep learning-based approaches have been…
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…
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Tobias Czempiel, Magdalini Paschali, Matthias Keicher +4
Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support…