Zobrazeno 1 - 10
of 438
pro vyhledávání: '"P Regitnig"'
Autor:
Markus Plass, Michaela Kargl, Tim‐Rasmus Kiehl, Peter Regitnig, Christian Geißler, Theodore Evans, Norman Zerbe, Rita Carvalho, Andreas Holzinger, Heimo Müller
Publikováno v:
The Journal of Pathology: Clinical Research, Vol 9, Iss 4, Pp 251-260 (2023)
Abstract The current move towards digital pathology enables pathologists to use artificial intelligence (AI)‐based computer programmes for the advanced analysis of whole slide images. However, currently, the best‐performing AI algorithms for imag
Externí odkaz:
https://doaj.org/article/a841ca914a6845ad930c026739ad0fef
Autor:
Wulczyn, Ellery, Nagpal, Kunal, Symonds, Matthew, Moran, Melissa, Plass, Markus, Reihs, Robert, Nader, Farah, Tan, Fraser, Cai, Yuannan, Brown, Trissia, Flament-Auvigne, Isabelle, Amin, Mahul B., Stumpe, Martin C., Muller, Heimo, Regitnig, Peter, Holzinger, Andreas, Corrado, Greg S., Peng, Lily H., Chen, Po-Hsuan Cameron, Steiner, David F., Zatloukal, Kurt, Liu, Yun, Mermel, Craig H.
Publikováno v:
Nature Communications Medicine (2021)
Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with
Externí odkaz:
http://arxiv.org/abs/2012.05197
Autor:
Wulczyn, Ellery, Steiner, David F., Moran, Melissa, Plass, Markus, Reihs, Robert, Tan, Fraser, Flament-Auvigne, Isabelle, Brown, Trissia, Regitnig, Peter, Chen, Po-Hsuan Cameron, Hegde, Narayan, Sadhwani, Apaar, MacDonald, Robert, Ayalew, Benny, Corrado, Greg S., Peng, Lily H., Tse, Daniel, Müller, Heimo, Xu, Zhaoyang, Liu, Yun, Stumpe, Martin C., Zatloukal, Kurt, Mermel, Craig H.
Publikováno v:
Nature Partner Journal Digital Medicine (2021)
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease specific survival for stage II and III colorecta
Externí odkaz:
http://arxiv.org/abs/2011.08965
Akademický článek
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Autor:
Andreas Holzinger, Michaela Kargl, Bettina Kipperer, Peter Regitnig, Markus Plass, Heimo Muller
Publikováno v:
IEEE Access, Vol 10, Pp 23732-23747 (2022)
Personas have successfully supported the development of classical user interfaces for more than two decades by mapping users’ mental models to specific contexts. The rapid proliferation of Artificial Intelligence (AI) applications makes it necessar
Externí odkaz:
https://doaj.org/article/818ec647676a4fda99b2f6104cd8f2cd
Autor:
Martin Zacharias, Karl Kashofer, Philipp Wurm, Peter Regitnig, Moritz Schütte, Margit Neger, Sandra Ehmann, Leigh M. Marsh, Grazyna Kwapiszewska, Martina Loibner, Anna Birnhuber, Eva Leitner, Andrea Thüringer, Elke Winter, Stefan Sauer, Marion J. Pollheimer, Fotini R. Vagena, Carolin Lackner, Barbara Jelusic, Lesley Ogilvie, Marija Durdevic, Bernd Timmermann, Hans Lehrach, Kurt Zatloukal, Gregor Gorkiewicz
Publikováno v:
iScience, Vol 25, Iss 9, Pp 104926- (2022)
Summary: Secondary infections contribute significantly to covid-19 mortality but driving factors remain poorly understood. Autopsies of 20 covid-19 cases and 14 controls from the first pandemic wave complemented with microbial cultivation and RNA-seq
Externí odkaz:
https://doaj.org/article/3b9ab54a489a475a9778db17045793d4
Autor:
Martina Loibner, Paul Barach, Stella Wolfgruber, Christine Langner, Verena Stangl, Julia Rieger, Esther Föderl-Höbenreich, Melina Hardt, Eva Kicker, Silvia Groiss, Martin Zacharias, Philipp Wurm, Gregor Gorkiewicz, Peter Regitnig, Kurt Zatloukal
Publikováno v:
Frontiers in Psychology, Vol 13 (2022)
The SARS-CoV-2 pandemic has highlighted the interdependency of healthcare systems and research organizations on manufacturers and suppliers of personnel protective equipment (PPE) and the need for well-trained personnel who can react quickly to chang
Externí odkaz:
https://doaj.org/article/5851fbe5a6954013b04e3ce853786881
Autor:
Ellery Wulczyn, David F. Steiner, Melissa Moran, Markus Plass, Robert Reihs, Fraser Tan, Isabelle Flament-Auvigne, Trissia Brown, Peter Regitnig, Po-Hsuan Cameron Chen, Narayan Hegde, Apaar Sadhwani, Robert MacDonald, Benny Ayalew, Greg S. Corrado, Lily H. Peng, Daniel Tse, Heimo Müller, Zhaoyang Xu, Yun Liu, Martin C. Stumpe, Kurt Zatloukal, Craig H. Mermel
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (2021)
Abstract Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III
Externí odkaz:
https://doaj.org/article/da537cd907ad48cb854376055689af37
Akademický článek
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Akademický článek
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