The influence of a specific ophthalmological electronic health record on ICD-10 coding. - Karsten Kortüm; Christoph Hirneiß; Michael Müller; Alexander Babenko; Anselm Kampik; Thomas C. Kreutzer (BMC Medical Informatics Decis. Mak., 2016)

Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction. - Anees Kazi; Shadi Albarqouni; Karsten U. Kortuem; Nassir Navab (CoRR, 2018)

InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction. - Anees Kazi; Shayan Shekarforoush; S. Arvind Krishna; Hendrik Burwinkel; Gerome Vivar; Karsten Kortüm; Seyed-Ahmad Ahmadi; Shadi Albarqouni; Nassir Navab (IPMI, 2019)

Self-Attention Equipped Graph Convolutions for Disease Prediction. - Anees Kazi; S. Arvind Krishna; Shayan Shekarforoush; Karsten U. Kortuem; Shadi Albarqouni; Nassir Navab (ISBI, 2019)

Graph Convolution Based Attention Model for Personalized Disease Prediction. - Anees Kazi; Shayan Shekarforoush; S. Arvind Krishna; Hendrik Burwinkel; Gerome Vivar; Benedikt Wiestler; Karsten Kortüm; Seyed-Ahmad Ahmadi; Shadi Albarqouni; Nassir Navab (MICCAI (4), 2019)

Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation. - Ilja Manakov; Markus Rohm; Christoph Kern; Benedikt Schworm; Karsten U. Kortuem; Volker Tresp (DART/MIL3ID@MICCAI, 2019)

Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy. - Olle G. Holmberg; Niklas D. Köhler; Thiago Martins; Jakob Siedlecki; Tina Herold; Leonie Keidel; Ben Asani; Johannes Schiefelbein; Siegfried Priglinger; Karsten U. Kortuem; Fabian J. Theis (Nat. Mach. Intell., 2020)