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Clemens Ammann

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Frontiers in Cardiovascular Medicine|May 5, 2023
Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variationsClemens Ammann, Thomas Hadler, Jan Gröschel, et al.
Scandinavian Cardiovascular Journal : SCJ|July 15, 2022
Fast acquisition of left and right ventricular function parameters applying cardiovascular magnetic resonance in clinical routine - validation of a 2-shot compressed sensing cine sequenceJan Gröschel, Clemens Ammann, Leonora Zange, et al.
Plos One|May 16, 2025
Verity plots: A novel method of visualizing reliability assessments of artificial intelligence methods in quantitative cardiovascular magnetic resonanceThomas Hadler, Clemens Ammann, Hadil Saad, et al.
Scientific Reports|June 2, 2026
A comparison of vendor artificial intelligence solutions for automated post-processing of short-axis cine images in cardiovascular magnetic resonance imagingThomas Hadler, Clemens Ammann, Hadil Saad, et al.
Journal of Imaging Informatics in Medicine|March 18, 2025
Lumos: Software for Multi-level Multi-reader Comparison of Cardiovascular Magnetic Resonance Late Gadolinium Enhancement Scar QuantificationPhiline Reisdorf, Jonathan Gavrysh, Clemens Ammann, et al.
Scientific Reports|February 6, 2023
Introduction of a cascaded segmentation pipeline for parametric T1 mapping in cardiovascular magnetic resonance to improve segmentation performanceDarian Viezzer, Thomas Hadler, Clemens Ammann, et al.
The International Journal of Cardiovascular Imaging|December 25, 2025
Assessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypesHadil Saad, Clemens Ammann, Thomas Hadler, et al.
Computer Methods and Programs in Biomedicine|May 31, 2023
Lazy Luna: Extendible software for multilevel reader comparison in cardiovascular magnetic resonance imagingThomas Hadler, Clemens Ammann, Jens Wetzl, et al.
Ebiomedicine|March 15, 2024
Post-hoc standardisation of parametric T1 maps in cardiovascular magnetic resonance imaging: a proof-of-conceptDarian Viezzer, Thomas Hadler, Jan Gröschel, et al.
CJC Open|September 2, 2025
Shaping Quality in Cardiovascular Magnetic Resonance: A Comparative Study of Segmentation Approaches by Trainees and ExpertsJan Gröschel, Thomas Hadler, Leonhard Grassow, et al.
Pageof 2

Showing results (1-10 of 17) with videos related to

Sort By:
Pageof 2
Frontiers in Cardiovascular Medicine|May 5, 2023
Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variationsClemens Ammann, Thomas Hadler, Jan Gröschel, et al.
Scandinavian Cardiovascular Journal : SCJ|July 15, 2022
Fast acquisition of left and right ventricular function parameters applying cardiovascular magnetic resonance in clinical routine - validation of a 2-shot compressed sensing cine sequenceJan Gröschel, Clemens Ammann, Leonora Zange, et al.
Plos One|May 16, 2025
Verity plots: A novel method of visualizing reliability assessments of artificial intelligence methods in quantitative cardiovascular magnetic resonanceThomas Hadler, Clemens Ammann, Hadil Saad, et al.
Scientific Reports|June 2, 2026
A comparison of vendor artificial intelligence solutions for automated post-processing of short-axis cine images in cardiovascular magnetic resonance imagingThomas Hadler, Clemens Ammann, Hadil Saad, et al.
Journal of Imaging Informatics in Medicine|March 18, 2025
Lumos: Software for Multi-level Multi-reader Comparison of Cardiovascular Magnetic Resonance Late Gadolinium Enhancement Scar QuantificationPhiline Reisdorf, Jonathan Gavrysh, Clemens Ammann, et al.
Scientific Reports|February 6, 2023
Introduction of a cascaded segmentation pipeline for parametric T1 mapping in cardiovascular magnetic resonance to improve segmentation performanceDarian Viezzer, Thomas Hadler, Clemens Ammann, et al.
The International Journal of Cardiovascular Imaging|December 25, 2025
Assessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypesHadil Saad, Clemens Ammann, Thomas Hadler, et al.
Computer Methods and Programs in Biomedicine|May 31, 2023
Lazy Luna: Extendible software for multilevel reader comparison in cardiovascular magnetic resonance imagingThomas Hadler, Clemens Ammann, Jens Wetzl, et al.
Ebiomedicine|March 15, 2024
Post-hoc standardisation of parametric T1 maps in cardiovascular magnetic resonance imaging: a proof-of-conceptDarian Viezzer, Thomas Hadler, Jan Gröschel, et al.
CJC Open|September 2, 2025
Shaping Quality in Cardiovascular Magnetic Resonance: A Comparative Study of Segmentation Approaches by Trainees and ExpertsJan Gröschel, Thomas Hadler, Leonhard Grassow, et al.
Pageof 2