Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians

• URL: http://arxiv.org/abs/2312.02225v2
• Date: Thu, 28 Dec 2023 11:44:50 GMT
• Title: Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians
• Authors: Alessandro Farace di Villaforesta, Lucie Charlotte Magister, Pietro Barbiero, Pietro Li\`o
• Abstract summary: We provide global explanations for Graph Neural Networks using GCExplainer and Logic Explained Networks. By training on H&E slides of breast cancer, we show promising results in offering explainable and trustworthy AI tools for clinicians.
• Score: 54.136225756724755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To address the challenge of the ``black-box" nature of deep learning in medical settings, we combine GCExplainer - an automated concept discovery solution - along with Logic Explained Networks to provide global explanations for Graph Neural Networks. We demonstrate this using a generally applicable graph construction and classification pipeline, involving panoptic segmentation with HoVer-Net and cancer prediction with Graph Convolution Networks. By training on H&E slides of breast cancer, we show promising results in offering explainable and trustworthy AI tools for clinicians.

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