DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation
Abstract
Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous,...
Paper Details
Title
DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation
Published Date
Jul 6, 2020
Journal
Volume
10
Issue
1
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