  We present HoVer-UNet, an approach to distill the knowledge of the
multi-branch HoVerNet framework for nuclei instance segmentation and
classification in histopathology. We propose a compact, streamlined single UNet
network with a Mix Vision Transformer backbone, and equip it with a custom loss
function to optimally encode the distilled knowledge of HoVerNet, reducing
computational requirements without compromising performances. We show that our
model achieved results comparable to HoVerNet on the public PanNuke and Consep
datasets with a three-fold reduction in inference time. We make the code of our
model publicly available at https://github.com/DIAGNijmegen/HoVer-UNet.
