Article

Interpreting convolutional neural network explainability for head-and-neck cancer radiotherapy organ-at-risk segmentation

2025-07-31

Abstract excerpt

<h4>Background</h4> Convolutional neural networks (CNNs) have emerged to reduce clinical resources and standardize auto-contouring of organs-at-risk (OARs). Although CNNs perform adequately for most patients, understanding when the CNN might fail is critical for effective and safe clinical deployment. However, the limitations of CNNs are poorly understood because of their black-box nature. Explainable artificial...

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Literature Corpus work: 5d47d299-0517-5efa-9fa1-ac953466adf6
DOI: 10.1101/2025.07.30.25332421

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