  Identification and quantification of nuclei in colorectal cancer haematoxylin
\& eosin (H\&E) stained histology images is crucial to prognosis and patient
management. In computational pathology these tasks are referred to as nuclear
segmentation, classification and composition and are used to extract meaningful
interpretable cytological and architectural features for downstream analysis.
The CoNIC challenge poses the task of automated nuclei segmentation,
classification and composition into six different types of nuclei from the
largest publicly known nuclei dataset - Lizard. In this regard, we have
developed pipelines for the prediction of nuclei segmentation using HoVer-Net
and ALBRT for cellular composition. On testing on the preliminary test set,
HoVer-Net achieved a PQ of 0.58, a PQ+ of 0.58 and finally a mPQ+ of 0.35. For
the prediction of cellular composition with ALBRT on the preliminary test set,
we achieved an overall $R^2$ score of 0.53, consisting of 0.84 for lymphocytes,
0.70 for epithelial cells, 0.70 for plasma and .060 for eosinophils.
