Combining a differentiable dose-volume histogram loss with an adversarial loss improves a neural network's ability to predict Pareto-optimal radiation dose distributions for prostate IMRT.
MSE loss is a generalized, domain-agnostic loss function that can be applied to many problems in many domains
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Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy
Combining a differentiable dose-volume histogram loss with an adversarial loss improves a neural network's ability to predict Pareto-optimal radiation dose distributions for prostate IMRT.