  The success of supervised deep learning models in medical image segmentation
relies on detailed annotations. However, labor-intensive manual labeling is
costly and inefficient, especially in dense object segmentation. To this end,
we propose a self-supervised learning based approach with a Prior
Self-activation Module (PSM) that generates self-activation maps from the input
images to avoid labeling costs and further produce pseudo masks for the
downstream task. To be specific, we firstly train a neural network using
self-supervised learning and utilize the gradient information in the shallow
layers of the network to generate self-activation maps. Afterwards, a
semantic-guided generator is then introduced as a pipeline to transform visual
representations from PSM to pixel-level semantic pseudo masks for downstream
tasks. Furthermore, a two-stage training module, consisting of a nuclei
detection network and a nuclei segmentation network, is adopted to achieve the
final segmentation. Experimental results show the effectiveness on two public
pathological datasets. Compared with other fully-supervised and
weakly-supervised methods, our method can achieve competitive performance
without any manual annotations.
