  This manuscript gives a brief description of the algorithm used to
participate in CoNIC Challenge 2022. After the baseline was made available, we
follow the method in it and replace the ResNet baseline with ConvNeXt one.
Moreover, we propose to first convert RGB space to Haematoxylin-Eosin-DAB(HED)
space, then use Haematoxylin composition of origin image to smooth semantic one
hot label. Afterwards, nuclei distribution of train and valid set are explored
to select the best fold split for training model for final test phase
submission. Results on validation set shows that even with channel of each
stage smaller in number, HoVerNet with ConvNeXt-tiny backbone still improves
the mPQ+ by 0.04 and multi r2 by 0.0144
