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

• URL: http://arxiv.org/abs/2111.12854v1
• Date: Thu, 25 Nov 2021 00:34:46 GMT
• Title: Extending the Relative Seriality Formalism for Interpretable Deep Learning of Normal Tissue Complication Probability Models
• Authors: Tahir I. Yusufaly
• Abstract summary: We show 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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