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Cervical Cell Detection Benchmark with Effective Feature Representation

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Cognitive Systems and Signal Processing (ICCSIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1397))

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Abstract

As deep convolutional neural networks have shown promising performance in medical image analysis, a number of deep learning based cervical cytology diagnosis methods were developed in recent years. Most studies have achieved available performance in cell classification or segmentation, however, there still exists some challenges for effective screening. Cervical cell detection is a more significant task in cytology diagnosis for cancers. In this paper, we propose a detection framework with effective feature representation for automatic cervical cytology analysis. We employ elastic transformation and a channel and spacial attention module to obtain a more powerful feature extractor. The experimental results demonstrate the efficiency and accuracy improved by our effective feature representation.

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Authors and Affiliations

  1. Computer Vision Institute, College of Computer Science and Software Engineering of Shenzhen University, Shenzhen, China

    Menglu Zhang & Linlin Shen

Authors
  1. Menglu Zhang
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  2. Linlin Shen
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Corresponding authors

Correspondence to Menglu Zhang or Linlin Shen .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Tsinghua University, Beijing, China

    Huaping Liu

  3. Tsinghua University, Beijing, China

    Bin Fang

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Zhang, M., Shen, L. (2021). Cervical Cell Detection Benchmark with Effective Feature Representation. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_38

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  • DOI: https://doi.org/10.1007/978-981-16-2336-3_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2335-6

  • Online ISBN: 978-981-16-2336-3

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