Abstract
Medical imaging analysis has witnessed impressive progress in recent years thanks to the development of large-scale labeled datasets. However, in many fields, including cervical cytology, a large well-annotated benchmark dataset remains missing. In this paper, we introduce by far the largest cervical cytology dataset, called Deep Cervical Cytological Lesions (referred to as DCCL). DCCL contains 14,432 image patches with around \(1{,}200\times 2{,}000\) pixels cropped from 1,167 whole slide images collected from four medical centers and scanned by one of the three kinds of digital slide scanners. Besides patch level labels, cell level labels are provided, with 27,972 lesion cells labeled based on The 2014 Bethesda System and the bounding box by six board-certified pathologists with eight years of experience on the average. We also use deep learning models to generate the baseline performance for lesion cell detection and cell type classification on DCCL. We believe this dataset can serve as a valuable resource and platform for researchers to develop new algorithms and pipelines for advanced cervical cancer diagnosis and prevention.
Keywords
- Cervical cancer screening
- Liquid-based cytology
- Deep learning
C. Zhang and D. Liu—Equal contribution.
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Bora, K., et al.: Pap smear image classification using convolutional neural network. In: 10th ICVGIP, p. 55. ACM (2016)
Bray, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 68(6), 394–424 (2018)
Chen, Y.F., et al.: Semi-automatic segmentation and classification of pap smear cells. IEEE J. Biomed. Health Inform. 18, 94–108 (2014)
Everingham, M., et al.: The pascal visual object classes (VOC) challenge. IJCV 88(2), 303–338 (2010)
He, K., et al.: Deep residual learning for image recognition. In: CVPR (2016)
Huang, G., et al.: Densely connected convolutional networks. In: CVPR (2017)
Jantzen, J., et al.: Pap-smear benchmark data for pattern classification. NiSIS (2005)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Lin, T.-Y., et al.: Focal loss for dense object detection. In: CVPR (2017)
Meiquan, X., et al.: Cervical cytology intelligent diagnosis based on object detection technology (2018)
Nayar, R., et al.: The Pap Test and Bethesda 2014. Acta Cytologica (2015)
Phoulady, H.A., et al.: A new cervical cytology dataset for nucleus detection and image classification (Cervix93) and methods for cervical nucleus detection. arXiv preprint arXiv:1811.09651 (2018)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks (2015)
Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Med. Image Anal. 42, 1–13 (2017)
Song, Y., et al.: Automated segmentation of overlapping cytoplasm in cervical smear images via contour fragments. In: AAAI 2018 (2018)
Szegedy, C., et al.: Rethinking the inception architecture for computer vision. In: CVPR (2016)
Tucker, J.: CERVISCAN: an image analysis system for experiments in automatic cervical smear prescreening. Comput. Biomed. Res. 9(2), 93–107 (1976)
Zhang, L., et al.: Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining. Cytometry Part A 85, 214–230 (2014)
Zhang, L., et al.: DeepPap: deep convolutional networks for cervical cell classification. IEEE J. Biomed. Health Inform. 21, 1633–1643 (2017)
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Zhang, C. et al. (2019). DCCL: A Benchmark for Cervical Cytology Analysis. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_8
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DOI: https://doi.org/10.1007/978-3-030-32692-0_8
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