Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Johannes Hofmanninger

Showing results (1-10 of 12) with videos related to

Pageof 2
Sort By:
European Radiology|March 18, 2021
Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after traumaSebastian Röhrich, Johannes Hofmanninger, Lukas Negrin, et al.
European Radiology|May 4, 2021
Correction to: Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after traumaSebastian Röhrich, Johannes Hofmanninger, Lukas Negrin, et al.
Radiology. Cardiothoracic Imaging|March 29, 2021
Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CTSebastian Röhrich, Johannes Hofmanninger, Florian Prayer, et al.
European Radiology Experimental|August 21, 2020
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemJohannes Hofmanninger, Forian Prayer, Jeanny Pan, et al.
Nature Communications|September 29, 2021
Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imagingMatthias Perkonigg, Johannes Hofmanninger, Christian J Herold, et al.
Respiratory Physiology & Neurobiology|August 15, 2013
Heterogeneity and matching of ventilation and perfusion within anatomical lung units in ratsRobb W Glenny, Christian Bauer, Johannes Hofmanninger, et al.
Der Radiologe|January 10, 2020
[Machine learning in radiology : Terminology from individual timepoint to trajectory]Georg Langs, Ulrike Attenberger, Roxane Licandro, et al.
Methods (San Diego, Calif.)|September 6, 2020
Variability of computed tomography radiomics features of fibrosing interstitial lung disease: A test-retest studyFlorian Prayer, Johannes Hofmanninger, Michael Weber, et al.
Plos One|August 2, 2017
Effects of individualized electrical impedance tomography and image reconstruction settings upon the assessment of regional ventilation distribution: Comparison to 4-dimensional computed tomography in a porcine modelFlorian Thürk, Stefan Boehme, Daniel Mudrak, et al.
European Radiology|September 6, 2022
Unsupervised machine learning identifies predictive progression markers of IPFJeanny Pan, Johannes Hofmanninger, Karl-Heinz Nenning, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
European Radiology|March 18, 2021
Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after traumaSebastian Röhrich, Johannes Hofmanninger, Lukas Negrin, et al.
European Radiology|May 4, 2021
Correction to: Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after traumaSebastian Röhrich, Johannes Hofmanninger, Lukas Negrin, et al.
Radiology. Cardiothoracic Imaging|March 29, 2021
Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CTSebastian Röhrich, Johannes Hofmanninger, Florian Prayer, et al.
European Radiology Experimental|August 21, 2020
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemJohannes Hofmanninger, Forian Prayer, Jeanny Pan, et al.
Nature Communications|September 29, 2021
Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imagingMatthias Perkonigg, Johannes Hofmanninger, Christian J Herold, et al.
Respiratory Physiology & Neurobiology|August 15, 2013
Heterogeneity and matching of ventilation and perfusion within anatomical lung units in ratsRobb W Glenny, Christian Bauer, Johannes Hofmanninger, et al.
Der Radiologe|January 10, 2020
[Machine learning in radiology : Terminology from individual timepoint to trajectory]Georg Langs, Ulrike Attenberger, Roxane Licandro, et al.
Methods (San Diego, Calif.)|September 6, 2020
Variability of computed tomography radiomics features of fibrosing interstitial lung disease: A test-retest studyFlorian Prayer, Johannes Hofmanninger, Michael Weber, et al.
Plos One|August 2, 2017
Effects of individualized electrical impedance tomography and image reconstruction settings upon the assessment of regional ventilation distribution: Comparison to 4-dimensional computed tomography in a porcine modelFlorian Thürk, Stefan Boehme, Daniel Mudrak, et al.
European Radiology|September 6, 2022
Unsupervised machine learning identifies predictive progression markers of IPFJeanny Pan, Johannes Hofmanninger, Karl-Heinz Nenning, et al.
Pageof 2