18 citations · 50 across the 8 of their papers we have counts for
8 papers
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…
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…
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
Gerome Vivar, Anees Kazi, Hendrik Burwinkel +3
Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve as enablers of clinical decisio…
Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions
Hendrik Burwinkel, Holger Matz, Stefan Saur +6
Today, cataract surgery is the most frequently performed ophthalmic surgery in the world. The cataract, a developing opacity of the human eye lens, constitutes the world's most fre…
Decision Support for Intoxication Prediction Using Graph Convolutional Networks
Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni +4
Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive natur…
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
Gerome Vivar, Hendrik Burwinkel, Anees Kazi +3
Clinical diagnostic decision making and population-based studies often rely on multi-modal data which is noisy and incomplete. Recently, several works proposed geometric deep learn…
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks
Hendrik Burwinkel, Anees Kazi, Gerome Vivar +4
Recently, Geometric Deep Learning (GDL) has been introduced as a novel and versatile framework for computer-aided disease classification. GDL uses patient meta-information such as…
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Anees Kazi, Shayan shekarforoush, S. Arvind krishna +6
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GC…