Tumor tissue segmentation for histopathological images

Histopathological image analysis is considered as a gold standard for cancer identification and diagnosis. Tumor segmentation for histopathological images is one of the most important research topics and its performance directly affects the diagnosis judgment of doctors for cancer categories and their periods. With the remarkable development of deep learning methods, extensive methods have been proposed for tumor segmentation. However, there are few researches on analysis of specific pipeline of tumor segmentation. Moreover, few studies have done detailed research on the hard example mining of tumor segmentation. In order to bridge this gap, this study firstly summarize a specific pipeline of tumor segmentation. Then, hard example mining in tumor segmentation is also explored. Finally, experiments are conducted for evaluating segmentation performance of our method, demonstrating the effects of our method and hard example mining.