  We formally demonstrate that the relative seriality model of Kallman, et al.
maps exactly onto a simple type of convolutional neural network. This approach
leads to a natural interpretation of feedforward connections in the
convolutional layer and stacked intermediate pooling layers in terms of
bystander effects and hierarchical tissue organization, respectively. These
results serve as proof-of-principle for radiobiologically interpretable deep
learning of normal tissue complication probability using large-scale imaging
and dosimetry datasets.
