Cancer is one of the leading causes of death in the developed world. Cancer
diagnosis is performed through the microscopic analysis of a sample of
suspicious tissue. This process is time consuming and error prone, but Deep
Learning models could be helpful for pathologists during cancer diagnosis. We
propose to change the CenterNet2 object detection model to also perform
instance segmentation, which we call SegCenterNet2. We train SegCenterNet2 in
the CoNIC challenge dataset and show that it performs better than Mask R-CNN in
the competition metrics.