Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Johannes Hofmanninger"'
Autor:
Matthias Perkonigg, Johannes Hofmanninger, Christian J. Herold, James A. Brink, Oleg Pianykh, Helmut Prosch, Georg Langs
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a
Externí odkaz:
https://doaj.org/article/f9fc7f5d825149758843a206d878d863
Autor:
Johannes Hofmanninger, Forian Prayer, Jeanny Pan, Sebastian Röhrich, Helmut Prosch, Georg Langs
Publikováno v:
European Radiology Experimental, Vol 4, Iss 1, Pp 1-13 (2020)
Abstract Background Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on differen
Externí odkaz:
https://doaj.org/article/6b04fd0a1dc247348bfabb653d3067d0
Autor:
Florian Thürk, Stefan Boehme, Daniel Mudrak, Stefan Kampusch, Alice Wielandner, Helmut Prosch, Christina Braun, Frédéric P R Toemboel, Johannes Hofmanninger, Eugenijus Kaniusas
Publikováno v:
PLoS ONE, Vol 12, Iss 8, p e0182215 (2017)
Electrical impedance tomography (EIT) is a promising imaging technique for bedside monitoring of lung function. It is easily applicable, cheap and requires no ionizing radiation, but clinical interpretation of EIT-images is still not standardized. On
Externí odkaz:
https://doaj.org/article/a5470f51f7444b76ae24a3bff2d35196
Autor:
Jeanny Pan, Johannes Hofmanninger, Karl-Heinz Nenning, Florian Prayer, Sebastian Röhrich, Nicola Sverzellati, Venerino Poletti, Sara Tomassetti, Michael Weber, Helmut Prosch, Georg Langs
Publikováno v:
Pan, J, Hofmanninger, J, Nenning, K H, Prayer, F, Röhrich, S, Sverzellati, N, Poletti, V, Tomassetti, S, Weber, M, Prosch, H & Langs, G 2023, ' Unsupervised machine learning identifies predictive progression markers of IPF ', European Radiology, vol. 33, no. 2, pp. 925-935 . https://doi.org/10.1007/s00330-022-09101-x
Objectives To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome. Method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85b0379026b19d13015b51f541e6185c
https://pure.au.dk/portal/da/publications/unsupervised-machine-learning-identifies-predictive-progression-markers-of-ipf(58ba3144-3971-4e05-9cea-e9af8d3e4544).html
https://pure.au.dk/portal/da/publications/unsupervised-machine-learning-identifies-predictive-progression-markers-of-ipf(58ba3144-3971-4e05-9cea-e9af8d3e4544).html
Autor:
Johannes Hofmanninger, Helmut Prosch, Jeanny Pan, Florian Prayer, Georg Langs, Daria Kifjak, Michael Weber, Sebastian Röhrich, Alexander Willenpart
Publikováno v:
Methods. 188:98-104
Objectives To investigate the intra- and inter-scanner repeatability and reproducibility of CT radiomics features (RF) of fibrosing interstitial lung disease (fILD). Methods For this prospective, IRB-approved test-retest study, CT data of sixty fILD
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::590a6146c581a5e559334bee477ae4e8
https://doi.org/10.1016/j.ymeth.2020.08.007
https://doi.org/10.1016/j.ymeth.2020.08.007
Publikováno v:
European Radiology
Objectives Acute respiratory distress syndrome (ARDS) constitutes a major factor determining the clinical outcome in polytraumatized patients. Early prediction of ARDS is crucial for timely supportive therapy to reduce morbidity and mortality. The ob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::757f3447870e9e2b11a95fd2a3015673
http://europepmc.org/articles/PMC8270830
http://europepmc.org/articles/PMC8270830
Autor:
H. Prosch, Johannes Hofmanninger, Mario Zusag, Matthias Perkonigg, Roxane Licandro, Ulrike I. Attenberger, Georg Langs, Daniel Sobotka, Sebastian Röhrich
Publikováno v:
Der Radiologe. 60:6-14
Zusammenfassung Methodisches Problem Maschinelles Lernen (ML) nimmt zunehmend Einzug in die Radiologie, um Aufgaben wie die automatische Detektion und Segmentation von diagnoserelevanten Bildmerkmalen, die Charakterisierung von Krankheits- und Behand
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e4b84469f9342af9c15e748bf4063b60
https://doi.org/10.1007/s00117-019-00624-x
https://doi.org/10.1007/s00117-019-00624-x
Autor:
James A. Brink, Johannes Hofmanninger, Oleg S. Pianykh, Georg Langs, Helmut Prosch, Christian J. Herold, Matthias Perkonigg
Publikováno v:
Nature Communications
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d97ced7725e46722fb8a7a3a56bd977
https://pubmed.ncbi.nlm.nih.gov/34584080
https://pubmed.ncbi.nlm.nih.gov/34584080
Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition settings. When training a deep learning model on an initial static training
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d43383bcf840aa463fb10c83bacb26b
Autor:
Helmut Prosch, Sebastian Röhrich, Johannes Hofmanninger, Florian Prayer, Henning Müller, Georg Langs
Publikováno v:
Radiol Cardiothorac Imaging
Chest CT scans are one of the most common medical imaging procedures. The automatic extraction and quantification of imaging features may help in diagnosis, prognosis of, or treatment decision in cardiovascular, pulmonary, and metabolic diseases. How
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b22dba40bc1365d90ca7ec29ad532ff
https://europepmc.org/articles/PMC7978018/
https://europepmc.org/articles/PMC7978018/