Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 4 Dec 2023.
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10 of 22 references.
References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2018Sanity Checks for Saliency Mapscited by 1,156
- 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
- 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
- 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
- 2018BACH: Grand Challenge on Breast Cancer Histology Imagescited by 131
- 2020PanNuke Dataset Extension, Insights and Baselinescited by 114
- 2021GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networkscited by 35
- 2022Encoding Concepts in Graph Neural Networkscited by 18
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