  Pathological diagnosis is the gold standard for cancer diagnosis, but it is
labor-intensive, in which tasks such as cell detection, classification, and
counting are particularly prominent. A common solution for automating these
tasks is using nucleus segmentation technology. However, it is hard to train a
robust nucleus segmentation model, due to several challenging problems, the
nucleus adhesion, stacking, and excessive fusion with the background. Recently,
some researchers proposed a series of automatic nucleus segmentation methods
based on point annotation, which can significant improve the model performance.
Nevertheless, the point annotation needs to be marked by experienced
pathologists. In order to take advantage of segmentation methods based on point
annotation, further alleviate the manual workload, and make cancer diagnosis
more efficient and accurate, it is necessary to develop an automatic nucleus
detection algorithm, which can automatically and efficiently locate the
position of the nucleus in the pathological image and extract valuable
information for pathologists. In this paper, we propose a W-shaped network for
automatic nucleus detection. Different from the traditional U-Net based method,
mapping the original pathology image to the target mask directly, our proposed
method split the detection task into two sub-tasks. The first sub-task maps the
original pathology image to the binary mask, then the binary mask is mapped to
the density mask in the second sub-task. After the task is split, the task's
difficulty is significantly reduced, and the network's overall performance is
improved.
