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DCCL: A Benchmark for Cervical Cytology Analysis

  • Changzheng Zhang
  • Dong Liu
  • Lanjun Wang
  • Yaoxin Li
  • Xiaoshi Chen
  • Rui Luo
  • Shuanlong Che
  • Hehua Liang
  • Yinghua Li
  • Si Liu
  • Dandan Tu
  • Guojun Qi
  • Pifu LuoEmail author
  • Jiebo LuoEmail author
Conference paper
  • 2.6k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11861)

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 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Changzheng Zhang
    • 1
  • Dong Liu
    • 4
  • Lanjun Wang
    • 2
  • Yaoxin Li
    • 1
  • Xiaoshi Chen
    • 1
  • Rui Luo
    • 3
  • Shuanlong Che
    • 4
  • Hehua Liang
    • 4
  • Yinghua Li
    • 4
  • Si Liu
    • 4
  • Dandan Tu
    • 1
  • Guojun Qi
    • 3
  • Pifu Luo
    • 4
    Email author
  • Jiebo Luo
    • 5
    Email author
  1. 1.HuaweiShenzhenChina
  2. 2.Huawei CanadaMarkhamCanada
  3. 3.FutureweiBellevueUSA
  4. 4.KingMed Diagnostics Co., Ltd.GuangzhouChina
  5. 5.University of RochesterRochesterUSA

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