  Deep learning-based methods are gaining traction in digital pathology, with
an increasing number of publications and challenges that aim at easing the work
of systematically and exhaustively analyzing tissue slides. These methods often
achieve very high accuracies, at the cost of requiring large annotated datasets
to train. This requirement is especially difficult to fulfill in the medical
field, where expert knowledge is essential. In this paper we focus on nuclei
segmentation, which generally requires experienced pathologists to annotate the
nuclear areas in gigapixel histological images. We propose an algorithm for
instance segmentation that uses pseudo-label segmentations generated
automatically from point annotations, as a method to reduce the burden for
pathologists. With the generated segmentation masks, the proposed method trains
a modified version of HoVer-Net model to achieve instance segmentation.
Experimental results show that the proposed method is robust to inaccuracies in
point annotations and comparison with Hover-Net trained with fully annotated
instance masks shows that a degradation in segmentation performance does not
always imply a degradation in higher order tasks such as tissue classification.
