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

arXiv: https://arxiv.org/abs/2111.12854 · HTML: https://mlsanity.com/paper/2111.12854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [A high-bias, low-variance introduction to Machine Learning for physicists](https://mlsanity.com/paper/1803.08823/) (2018)

## Abstract (arXiv)

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.