Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Matthias, Perkonigg"'
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:
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:
Christoph Fürböck, Matthias Perkonigg, Thomas Helbich, Katja Pinker, Valeria Romeo, Georg Langs
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164484
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::021effcba29a25a35c9d1d3afd1de076
https://doi.org/10.1007/978-3-031-16449-1_27
https://doi.org/10.1007/978-3-031-16449-1_27
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:
Savas Ozkan, N. Sinem Gezer, Dmitrii Lachinov, Debdoot Sheet, Fabian Isensee, Gozde Bozdagi Akar, M. Alper Selver, Soumick Chatterjee, Oliver Speck, A. Emre Kavur, Sinem Aslan, Josef Pauli, Oğuz Dicle, Gozde Unal, Pierre-Henri Conze, Andreas Nürnberger, Klaus H. Maier-Hein, Gurbandurdy Dovletov, Ronnie Rajan, Vladimir Groza, Rachana Sathish, Bora Baydar, Matthias Perkonigg, Shuo Han, Philipp Ernst, Duc Duy Pham, Mustafa Baris
Publikováno v:
Medical Image Analysis
Medical Image Analysis, Elsevier, 2021, 69, ⟨10.1016/j.media.2020.101950⟩
Medical Image Analysis, Elsevier, 2021, 69, ⟨10.1016/j.media.2020.101950⟩
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::062a182f507db13c73a8422353093c1a
https://hdl.handle.net/10278/3736173
https://hdl.handle.net/10278/3736173
Autor:
Georg, Langs, Ulrike, Attenberger, Roxane, Licandro, Johannes, Hofmanninger, Matthias, Perkonigg, Mario, Zusag, Sebastian, Röhrich, Daniel, Sobotka, Helmut, Prosch
Publikováno v:
Der Radiologe. 60(1)
Machine learning (ML) algorithms have an increasingly relevant role in radiology tackling tasks such as the automatic detection and segmentation of diagnosis-relevant markers, the quantification of progression and response, and their prediction in in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::efb0d99e9dd1dbfb97061f33c13f4bda
https://pubmed.ncbi.nlm.nih.gov/31915840
https://pubmed.ncbi.nlm.nih.gov/31915840
Autor:
James A. Brink, Johannes Hofmanninger, Christian J. Herold, Georg Langs, Matthias Perkonigg, Oleg S. Pianykh
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597122
MICCAI (2)
MICCAI (2)
In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance. Scanner manufacturer, reconstruction kernel, dose, other protocol specific settings or administering of contrast agents are ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28fc5e3003ecfbe896634336c8833b43
https://doi.org/10.1007/978-3-030-59713-9_35
https://doi.org/10.1007/978-3-030-59713-9_35
Publikováno v:
Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis ISBN: 9783030008062
DATRA/PIPPI@MICCAI
DATRA/PIPPI@MICCAI
The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we present a transfer learning approach using
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df84eebec5ccc3f909121a96519c333a
https://doi.org/10.1007/978-3-030-00807-9_3
https://doi.org/10.1007/978-3-030-00807-9_3