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 byst...

Full description

Saved in:
Bibliographic Details
Main Author Yusufaly, Tahir I
Format Journal Article
LanguageEnglish
Published 24.11.2021
Subjects
Computer Science - Computer Vision and Pattern Recognition
Physics - Biological Physics
Physics - Data Analysis, Statistics and Probability
Physics - Medical Physics
Quantitative Biology - Tissues and Organs
Online AccessGet full text

Cover

More Information
Summary: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.
DOI:10.48550/arxiv.2111.12854