🤖 AI Summary
The absence of systematic reviews on deep learning–based signet-ring cell (SRC) recognition hinders the alignment of algorithmic performance with clinical requirements. Method: This paper presents the first comprehensive, multi-task (classification, detection, segmentation) review of SRC identification studies from 2008 to 2023, covering the full research lifecycle and incorporating biological characteristics and recognition challenges. It introduces a clinically oriented performance evaluation framework and constructs a holistic analytical landscape for SRC analysis. Contribution/Results: The review identifies critical bottlenecks—including data scarcity, annotation inconsistency, and poor generalizability—and delineates the performance boundaries of state-of-the-art methods (e.g., CNNs, Transformers). It provides both a technology evolution roadmap and clinical translation guidelines for interdisciplinary research, thereby bridging computational innovation and pathological practice.
📝 Abstract
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.
