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

2021-11-25 00:34:46
Tahir I. Yusufaly
arXiv_CV
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2111.12854

PDF

https://arxiv.org/pdf/2111.12854.pdf

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