  We address the problem of automated nuclear segmentation, classification, and
quantification from Haematoxylin and Eosin stained histology images, which is
of great relevance for several downstream computational pathology applications.
In this work, we present a solution framed as a simultaneous semantic and
instance segmentation framework. Our solution is part of the Colon Nuclei
Identification and Counting (CoNIC) Challenge. We first train a semantic and
instance segmentation model separately. Our framework uses as backbone HoverNet
and Cascade Mask-RCNN models. We then ensemble the results with a custom
Non-Maximum Suppression embedding (NMS). In our framework, the semantic model
computes a class prediction for the cells whilst the instance model provides a
refined segmentation. We demonstrate, through our experimental results, that
our model outperforms the provided baselines by a large margin.
