Since signet ring cells (SRCs) are associated with high peripheral metastasis
rate and dismal survival, they play an important role in determining surgical
approaches and prognosis, while they are easily missed by even experienced
pathologists. Although automatic diagnosis SRCs based on deep learning has
received increasing attention to assist pathologists in improving the
diagnostic efficiency and accuracy, the existing works have not been
systematically overviewed, which hindered the evaluation of the gap between
algorithms and clinical applications. In this paper, we provide a survey on SRC
analysis driven by deep learning from 2008 to August 2023. Specifically, the
biological characteristics of SRCs and the challenges of automatic
identification are systemically summarized. Then, the representative algorithms
are analyzed and compared via dividing them into classification, detection, and
segmentation. Finally, for comprehensive consideration to the performance of
existing methods and the requirements for clinical assistance, we discuss the
open issues and future trends of SRC analysis. The retrospect research will
help researchers in the related fields, particularly for who without medical
science background not only to clearly find the outline of SRC analysis, but
also gain the prospect of intelligent diagnosis, resulting in accelerating the
practice and application of intelligent algorithms.

通过对深度学习驱动的代表性算法进行分析和比较，并讨论开放问题和未来趋势，本文回溯性研究有助于相关领域的研究人员，特别是那些没有医学科学背景的人，不仅能清晰地找到 SRC 分析的大纲，还能获得智能诊断的前景，从而加快智能算法的实践和应用。