643 citations · 1.1k across the 21 of their papers we have counts for
6 papers · 1 filter
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…
Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time
Gerome Vivar, Kamilia Mullakaeva, Andreas Zwergal +2
Computer-aided diagnosis (CADx) algorithms in medicine provide patient-specific decision support for physicians. These algorithms are usually applied after full acquisition of high…
Latent-Graph Learning for Disease Prediction
Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi +2
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction. A key component in thes…
Differentiable Graph Module (DGM) for Graph Convolutional Networks
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi +2
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. Such methods have sh…