Local and global feature aggregation for accurate epithelial cell classification using graph attention mechanisms in histopathology images

In digital pathology, cell-level tissue analyses are widely used to better understand tissue composition and structure. Publicly available datasets and models for cell detection and classification in colorectal cancer exist but lack the differentiation of normal and malignant epithelial cells that are important to perform prior to any downstream cell-based analysis. This classification task is particularly difficult due to the high intra-class variability of neoplastic cells. To tackle this, we present here a new method that uses graph-based node classification to take advantage of both local cell features and global tissue architecture to perform accurate epithelial cell classification. The proposed method demonstrated excellent performance on F1 score (PanNuke: 1.0, TCGA: 0.98) and performed significantly better than conventional computer vision methods (PanNuke: 0.99, TCGA: 0.92).