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

Frei, Ana Leni; Khan, Amjad; Studer, Linda; Zens, Philipp; Lugli, Alessandro; Fischer, Andreas; Zlobec, Inti (28 April 2023). Local and global feature aggregation for accurate epithelial cell classification using graph attention mechanisms in histopathology images. In: Medical Imaging with deep Learning 2023.

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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).

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5 Citations in Web of Science ®
6 Citations in Scopus

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