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Scientific Reports|September 2, 2020
Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-netNader Aldoj, Federico Biavati, Florian Michallek, et al.Scientific Reports|February 8, 2022
Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentationNader Aldoj, Federico Biavati, Marc Dewey, et al.Medical Physics|July 21, 2022
Active multitask learning with uncertainty-weighted loss for coronary calcium scoringBernhard Föllmer, Federico Biavati, Christian Wald, et al.Insights Into Imaging|November 19, 2023
Final-year medical students' perspective: a survey on the use of computed tomography in sepsisJulian Pohlan, Maria Isabel Opper Hernando, Roderic Waschinsky, et al.Journal of Cardiovascular Computed Tomography|June 18, 2026
Associations of segment-level coronary calcium scoring metrics with major and minor cardiovascular eventsSotirios Tsogias, Bernhard Föllmer, Kenrick Schulze, et al.Insights Into Imaging|October 16, 2024
Automated segment-level coronary artery calcium scoring on non-contrast CT: a multi-task deep-learning approachBernhard Föllmer, Sotirios Tsogias, Federico Biavati, et al.European Radiology|April 20, 2026
Detection of calcified plaques: comparison between coronary CT angiography and thin-slice non-contrast CT with deep learning-aided image registrationKenrick Schulze, Federico Biavati, Bernhard Föllmer, et al.Nature Reviews. Cardiology|July 18, 2023
Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteriesBernhard Föllmer, Michelle C Williams, Damini Dey, et al.Nature Reviews. Cardiology|June 5, 2023
Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a Consensus Statement from the Quantitative Cardiovascular Imaging Study GroupAldo J Vázquez Mézquita, Federico Biavati, Volkmar Falk, et al.Nature Reviews. Cardiology|August 2, 2025
Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study GroupKenrick Schulze, Anne-Marieke Stantien, Michelle C Williams, et al.Pageof 2