CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation. - Jennifer Faber; David Kügler; Emad Bahrami; Lea-Sophie Heinz; Dagmar Timmann; Thomas Michael Ernst; Katerina Deike-Hofmann; Thomas Klockgether; Bart van de Warrenburg; Judith van Gaalen; Kathrin Reetz; Sandro Romanzetti; et al. (NeuroImage, 2022)

Faithful Synthesis of Low-Dose Contrast-Enhanced Brain MRI Scans Using Noise-Preserving Conditional GANs. - Thomas Pinetz; Erich Kobler; Robert Haase 002; Katerina Deike-Hofmann; Alexander Radbruch; Alexander Effland (MICCAI (2), 2023)

Abstract: Deep Learning-based Detection of Vessel Occlusions on CT-Angiography in Patients with Suspected Acute Ischemic Stroke. - Gianluca Brugnara; Michael Baumgartner 001; Edwin D. Scholze; Katerina Deike-Hofmann; Klaus Kades; Jonas Scherer; Stefan Denner; Hagen Meredig; Aditya Rastogi; Mustafa A. Mahmutoglu; Christian Ulfert; Ulf Neuberger; et al. (Bildverarbeitung für die Medizin, 2024)

Gadolinium dose reduction for brain MRI using conditional deep learning. - Thomas Pinetz; Erich Kobler; Robert Haase 002; Julian A. Luetkens; Mathias Meetschen; Johannes Haubold; Cornelius Deuschl; Alexander Radbruch; Katerina Deike; Alexander Effland (CoRR, 2024)

Pre-examinations Improve Automated Metastases Detection on Cranial MRI. - Katerina Deike-Hofmann; Dorottya Dancs; Daniel Paech; Heinz-Peter Schlemmer; Klaus H. Maier-Hein; Philipp Bäumer; Alexander Radbruch; Michael Götz (CoRR, 2024)