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Nuclear segmentation, classification and quantification within Haematoxylin &Eosin stained histology images enables the extraction of interpretablecell-based features that can be used in downstream explainable models incomputational pathology (CPath). However, automatic recognition of differentnuclei is faced with a major challenge in that there are several differenttypes of nuclei, some of them exhibiting large intraclass variability. In thiswork, we propose an approach that combine Separable-HoverNet andInstance-YOLOv5 to indentify colon nuclei small and unbalanced. Our approachcan achieve mPQ+ 0.389 on the Segmentation and Classification-Preliminary TestDataset and r2 0.599 on the Cellular Composition-Preliminary Test Dataset onISBI 2022 CoNIC Challenge.

Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting