  Mitosis detection is one of the challenging problems in computational
pathology, and mitotic count is an important index of cancer grading for
pathologists. However, current counts of mitotic nuclei rely on pathologists
looking microscopically at the number of mitotic nuclei in hot spots, which is
subjective and time-consuming. In this paper, we propose a two-stage cascaded
network, named FoCasNet, for mitosis detection. In the first stage, a detection
network named M_det is proposed to detect as many mitoses as possible. In the
second stage, a classification network M_class is proposed to refine the
results of the first stage. In addition, the attention mechanism, normalization
method, and hybrid anchor branch classification subnet are introduced to
improve the overall detection performance. Our method achieves the current
highest F1-score of 0.888 on the public dataset ICPR 2012. We also evaluated
our method on the GZMH dataset released by our research team for the first time
and reached the highest F1-score of 0.563, which is also better than multiple
classic detection networks widely used at present. It confirmed the
effectiveness and generalization of our method. The code will be available at:
https://github.com/antifen/mitosis-nuclei-detection.
