  Nuclear segmentation and classification is an essential step for
computational pathology. TIA lab from Warwick University organized a nuclear
segmentation and classification challenge (CoNIC) for H&E stained
histopathology images in colorectal cancer with two highly correlated tasks,
nuclei segmentation and classification task and cellular composition task.
There are a few obstacles we have to address in this challenge, 1) limited
training samples, 2) color variation, 3) imbalanced annotations, 4) similar
morphological appearance among classes. To deal with these challenges, we
proposed a standardized pipeline for nuclear segmentation and classification by
integrating several pluggable components. First, we built a GAN-based model to
automatically generate pseudo images for data augmentation. Then we trained a
self-supervised stain normalization model to solve the color variation problem.
Next we constructed a baseline model HoVer-Net with cost-sensitive loss to
encourage the model pay more attention on the minority classes. According to
the results of the leaderboard, our proposed pipeline achieves 0.40665 mPQ+
(Rank 49th) and 0.62199 r2 (Rank 10th) in the preliminary test phase.
