Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians
This addresses the problem of interpretability for clinicians in digital histopathology, but it is incremental as it builds on existing methods.
The paper tackled the challenge of deep learning's black-box nature in medical settings by combining GCExplainer and Logic Explained Networks to provide global explanations for Graph Neural Networks, showing promising results in offering explainable and trustworthy AI tools for clinicians using breast cancer H&E slides.
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.