GrEp: Graph-based epithelial cell classification refinement in histopathology H&E images

• A novel graph-based workflow for the subclassification of epithelial cells into normal and malignant. • Node feature extraction using GammaFocus tile augmentation. • A clustering and median filtering post-processing to smooth predictions inside individual epithelial glands. • Expansion of two unseen tissue types (endometrium and pancreas). The automatic cell segmentation and classification from whole slide images plays an important role in digital pathology, unlocking new opportunities for biomarker discovery. Despite extensive research, this task faces persistent challenges such as the differentiation of epithelial cells into normal and malignant. Many existing models lack reporting of epithelial subtyping, and when available, their performance is often suboptimal. This work benchmarks state-of-the-art methods to highlight this limitation and introduces GrEp, a geometric deep learning strategy that considers the broader epithelium tissue architecture to infer cell-level classification rather than relying exclusively on nuclei morphology. The proposed graph-based workflow significantly outperformed state-of-the-art nuclei classification models in colorectal cancer and generalized effectively to two unseen tissue types, endometrium and pancreas, proving the robustness of the geometry-based model. Given its speed and accuracy, we believe GrEp to be a valuable method to refine epithelial cell classification for downstream analyses in clinical and research settings.