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PH-0606: AutoConfidence: Per-patient validation for clinical confidence in deep learning for radiotherapy.

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Title: PH-0606: AutoConfidence: Per-patient validation for clinical confidence in deep learning for radiotherapy.
Authors: Nix, M. (AUTHOR), Bird, D. (AUTHOR), Tyyger, M. (AUTHOR), Appelt, A. (AUTHOR), Murray, L. (AUTHOR), McCallum, H. (AUTHOR), Al-Qaisieh, B. (AUTHOR), Gooya, A. (AUTHOR)
Source: Radiotherapy & Oncology. 2020 Supplement 1, Vol. 152, pS342-S343. 2p.
Subjects: Deep learning, Confidence, Radiotherapy
Abstract: Poster Highlights: Poster highlights 20 PH: Autocontouring using deep learning PH-0606: AutoConfidence: Per-patient validation for clinical confidence in deep learning for radiotherapy M. Nix, D. Bird, M. Tyyger, A. Appelt, L. Murray, H. McCallum, B. Al-Qaisieh, A. Gooya. [Extracted from the article]
Copyright of Radiotherapy & Oncology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22Radiotherapy+%26+Oncology%22">Radiotherapy & Oncology</searchLink>. 2020 Supplement 1, Vol. 152, pS342-S343. 2p.
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  Data: Poster Highlights: Poster highlights 20 PH: Autocontouring using deep learning PH-0606: AutoConfidence: Per-patient validation for clinical confidence in deep learning for radiotherapy M. Nix, D. Bird, M. Tyyger, A. Appelt, L. Murray, H. McCallum, B. Al-Qaisieh, A. Gooya. [Extracted from the article]
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  Data: <i>Copyright of Radiotherapy & Oncology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/S0167-8140(21)00628-9
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        Text: English
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        StartPage: S342
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              Text: 2020 Supplement 1
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