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