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