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.

翻译：病理诊断是癌症诊断的黄金标准,但它是劳动密集型的,其中细胞检测、分类和计数等任务特别突出。将这些任务自动化的一个共同解决办法是使用核分解技术。然而,由于几个具有挑战性的问题,即核粘合、堆叠和与背景过度融合,很难训练一个强大的核分解模型。最近,一些研究人员提议了一系列基于点注的自动核分解方法,这可以显著改善模型性能。然而,点注需要由有经验的病理学家来标记。为了利用基于点注的分解方法,进一步减轻人工工作量,并使癌症诊断更高效和准确,有必要开发一个自动核分解模型模型,这可以自动和有效地定位核心在病理图像中的位置,并为病理学家提取有价值的信息。在本文件中,我们提议了一个W型模量网络网络自动检测网络。不同于传统的U-Net型方法,将原始病理图图直接绘制到目标掩码第二个掩码。为了利用分解方法,我们提出的方法将最初的检测任务分为两个路径图。