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Estimating uncertainty in radiation oncology dose prediction with dropout and bootstrap in U-Net models
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- Open access
- 1.86 M
John, Lee
[UCL]
Vanginderdeuren, Alyssa
[UCL]
Huet-Dastarac, Margerie
[UCL]
Barragan Montero, Ana Maria
[UCL]
| Document type | Communication à un colloque (Conference Paper) |
|---|---|
| Access type | Accès libre |
| Publication date | 2021 |
| Language | Anglais |
| Conference | "ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning", Online event (Bruges, Belgium) (du 6/10/2021 au 8/10/2021) |
| Journal information | "ESANN 2021 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning" |
| Peer reviewed | yes |
| Publication status | Publié |
| Affiliation | UCL - SSH/IACS - Institute of Analysis of Change in Contemporary and Historical Societies |
| Links |
| Bibliographic reference | John, Lee ; Vanginderdeuren, Alyssa ; Huet-Dastarac, Margerie ; Barragan Montero, Ana Maria. Estimating uncertainty in radiation oncology dose prediction with dropout and bootstrap in U-Net models.ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (Online event (Bruges, Belgium), du 6/10/2021 au 8/10/2021). In: ESANN 2021 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, |
|---|---|
| Permanent URL | http://hdl.handle.net/2078.1/257291 |