  Nuclei segmentation and classification have been a challenge in digital
pathology due to the specific domain characteristics. First, annotating a
large-scale dataset is quite consuming. It requires specific domain knowledge
and large efforts. Second, some nuclei are clustered together and hard to
segment from each other. Third, the classes are often extremely unbalanced. As
in Lizard, the number of epithelial nuclei is around 67 times larger than the
number of eosinophil nuclei. Fourth, the nuclei often exhibit high inter-class
similarity and intra-class variability. Connective nuclei may look very
different from each other while some of them share a similar shape with the
epithelial ones. Last but not least, pathological patches may have very
different color distributions among different datasets. Thus, a large-scale
generally annotated dataset and a specially-designed algorithm are needed to
solve this problem. The CoNIC challenge aims to promote the automatic
segmentation and classification task and requires researchers to develop
algorithms that perform segmentation, classification, and counting of 6
different types of nuclei with the large-scale annotated dataset: Lizard. Due
to the 60-minute time limit, the algorithm has to be simple and quick. In this
paper, we briefly describe the final method we used in the CoNIC challenge. Our
algorithm is based on Hover-Net and we added several modifications to it to
improve its performance.
