Abstract
Identifying signet ring cells on pathological images is an important clinical task that highly relevant to cancer grading and prognosis. However, it is challenging as the cells exhibit diverse visual appearance in the crowded cellular image. This task is also less studied by computational methods so far. This paper proposes a Classification Reinforcement Detection Network (CRDet) to alleviate the detection difficulties. CRDet is composed of a Cascade RCNN architecture and a dedicated devised Classification Reinforcement Branch (CRB), which consists of a dedicated context pool module and a corresponding feature enhancement classifier, aiming at extracting more comprehensive and discriminative features from the cell and its surrounding context. With the reinforced features, the small-sized cell can be well characterized, thus a better classification is expected. Experiments on a public signet ring cell dataset demonstrate the proposed CRDet achieves a better performance compared with popular CNN-based object detection models.
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Acknowledgements
This work was supported by the Natural Science Foundation of China (No. 61972333, 61876016, 61772526) and the Fundamental Research Funds for the Central Universities (2019JBZ110).
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Wang, S., Jia, C., Chen, Z., Gao, X. (2020). Signet Ring Cell Detection with Classification Reinforcement Detection Network. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_2
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