Extending the Relative Seriality Formalism for Interpretable Deep Learning of Normal Tissue Complication Probability Models

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.