Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond (English)
- New search for: Meyer, Anneke
- Further information on Meyer, Anneke:
- https://orcid.org/0000-0002-7477-3175
- New search for: Ghosh, Suhita
- Further information on Ghosh, Suhita:
- https://orcid.org/0000-0002-5553-585X
- New search for: Schindele, Daniel
- New search for: Schostak, Martin
- New search for: Stober, Sebastian
- New search for: Hansen, Christian
- New search for: Rak, Marko
- New search for: Meyer, Anneke
- New search for: Ghosh, Suhita
- New search for: Schindele, Daniel
- New search for: Schostak, Martin
- New search for: Stober, Sebastian
- New search for: Hansen, Christian
- New search for: Rak, Marko
In:
Artificial Intelligence in Medicine
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116
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2021
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ISSN:
- Article (Journal) / Electronic Resource
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Title:Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond
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Contributors:Meyer, Anneke ( author ) / Ghosh, Suhita ( author ) / Schindele, Daniel ( author ) / Schostak, Martin ( author ) / Stober, Sebastian ( author ) / Hansen, Christian ( author ) / Rak, Marko ( author )
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Published in:
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Publisher:
- New search for: Elsevier B.V.
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Publication date:2021-04-07
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ISSN:
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DOI:
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Type of media:Article (Journal)
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Source:
Table of contents – Volume 116
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
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Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representationsKarimi, Davood / Warfield, Simon K. / Gholipour, Ali et al. | 2021
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Editorial Board| 2021
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Explaining heterogeneity of individual treatment causal effects by subgroup discovery: An observational case study in antibiotics treatment of acute rhino-sinusitisQi, W. / Abu-Hanna, A. / van Esch, T.E.M. / de Beurs, D. / Liu, Y. / Flinterman, L.E. / Schut, M.C. et al. | 2021
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Fenchel duality of Cox partial likelihood with an application in survival kernel learningWilson, Christopher M. / Li, Kaiqiao / Sun, Qiang / Kuan, Pei Fen / Wang, Xuefeng et al. | 2021
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Evaluating diagnostic content of AI-generated radiology reports of chest X-raysBabar, Zaheer / van Laarhoven, Twan / Zanzotto, Fabio Massimo / Marchiori, Elena et al. | 2021
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Bayesian networks in healthcare: What is preventing their adoption?Kyrimi, Evangelia / Dube, Kudakwashe / Fenton, Norman / Fahmi, Ali / Neves, Mariana Raniere / Marsh, William / McLachlan, Scott et al. | 2021
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Coronary artery segmentation from intravascular optical coherence tomography using deep capsulesBalaji, Arjun / Kelsey, Lachlan J. / Majeed, Kamran / Schultz, Carl J. / Doyle, Barry J. et al. | 2021
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Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyondMeyer, Anneke / Ghosh, Suhita / Schindele, Daniel / Schostak, Martin / Stober, Sebastian / Hansen, Christian / Rak, Marko et al. | 2021