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Radiotherapy dose simulation using the Monte-Carlo technique surpasses existing algorithms in terms of precision but remains too time-consuming to be integrated in clinical workflows. We introduce a 3D recurrent and fully convolutional neural network architecture to produce high-precision Monte-Carlo-like dose simulations from low-precision and cheap-to-compute ones. We use the noise-to-noise setting, a weakly supervised training strategy, by training the models solely on low-precision data without expensive-to-compute, high-precision dose simulations. Several evaluation metrics are used to compare with other methods and to assess the clinical viability and quality of the generated dose maps.
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Friday 9th July
K1-9 (short): Learning with Noisy Labels and Limited Data - 16:45 - 17:30 (UTC+2)
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