  Segmentation and accurate localization of nuclei in histopathological images
is a very challenging problem, with most existing approaches adopting a
supervised strategy. These methods usually rely on manual annotations that
require a lot of time and effort from medical experts. In this study, we
present a self-supervised approach for segmentation of nuclei for whole slide
histopathology images. Our method works on the assumption that the size and
texture of nuclei can determine the magnification at which a patch is
extracted. We show that the identification of the magnification level for tiles
can generate a preliminary self-supervision signal to locate nuclei. We further
show that by appropriately constraining our model it is possible to retrieve
meaningful segmentation maps as an auxiliary output to the primary
magnification identification task. Our experiments show that with standard
post-processing, our method can outperform other unsupervised nuclei
segmentation approaches and report similar performance with supervised ones on
the publicly available MoNuSeg dataset. Our code and models are available
online to facilitate further research.
