Class-Controlled Copy-Paste Based Cell Segmentation for CoNIC Challenge

Abstract

Muti-class cell segmentation in histopathology images is a challenging task. Here, we propose a copy-paste augmentation-based method for CoNIC challenge. As the challenge train data is severely class imbalanced. To deal with it, we copy all cell objects of train data and paste them to the train image on the fly while training model. The paste strategy is that we paste more cell objects of the insufficient classes and paste less cell objects for the sufficient classes. We experimented the method by stratified splitting train data in 4:1 ratio, the result shows the copy paste method can reach PQ 64.84 and mPQ 53.72, which improved and 0.66 compared to without copy pasted. Moreover, the improvements in those insufficient classes is more obvious.