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DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Chao Li1, Xinggang Wang1, Wenyu Liu1

  • 1School of Electronics Information and Communications, Huazhong University of Science and Technology, Wuhan, PR China.

Medical Image Analysis
|February 19, 2018
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Summary

This study introduces a fast deep learning framework for automated mitosis detection in breast cancer histology slides, significantly improving accuracy and efficiency over manual methods.

Keywords:
Breast cancer gradingFaster R-CNNFully convolutional networkMitosis detection

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Digital histopathology

Background:

  • Manual mitosis counting is crucial for breast cancer prognostication but is labor-intensive and time-consuming.
  • Accurate and efficient mitosis detection is essential for reliable tumor aggressiveness assessment.

Purpose of the Study:

  • To develop and validate a novel multi-stage deep learning framework for accurate and rapid mitosis detection in breast cancer histopathological images.
  • To overcome limitations of manual counting and weak annotations in existing datasets.

Main Methods:

  • A multi-stage deep learning approach combining segmentation, detection, and verification networks.
  • Utilizing weak centroid labels for segmentation and contextual information for detection.
  • Employing a verification network to refine detection accuracy by removing false positives.

Main Results:

  • Achieved the highest F-score on the ICPR 2012 MITOSIS dataset using only the detection network.
  • Obtained state-of-the-art results on the ICPR 2014 MITOSIS dataset by fusing detection and verification models.
  • Demonstrated high speed and feasibility for clinical practice with GPU acceleration.

Conclusions:

  • The proposed deep learning framework offers a highly accurate and efficient solution for automated mitosis detection.
  • This method has the potential to significantly aid pathologists in breast cancer diagnosis and prognosis.
  • The framework's speed and accuracy make it suitable for integration into clinical workflows.