  Signet ring cell carcinoma is a type of rare adenocarcinoma with poor
prognosis. Early detection leads to huge improvement of patients' survival
rate. However, pathologists can only visually detect signet ring cells under
the microscope. This procedure is not only laborious but also prone to
omission. An automatic and accurate signet ring cell detection solution is thus
important but has not been investigated before. In this paper, we take the
first step to present a semi-supervised learning framework for the signet ring
cell detection problem. Self-training is proposed to deal with the challenge of
incomplete annotations, and cooperative-training is adapted to explore the
unlabeled regions. Combining the two techniques, our semi-supervised learning
framework can make better use of both labeled and unlabeled data. Experiments
on large real clinical data demonstrate the effectiveness of our design. Our
framework achieves accurate signet ring cell detection and can be readily
applied in the clinical trails. The dataset will be released soon to facilitate
the development of the area.
