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Related Experiment Video

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Breast cancer histopathology image classification through assembling multiple compact CNNs.

Chuang Zhu1, Fangzhou Song2, Ying Wang3

  • 1The Center for Data Science, the Beijing Key Laboratory of Network System Architecture and Convergence, the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road, Beijing, China. czhu@bupt.edu.cn.

BMC Medical Informatics and Decision Making
|October 24, 2019
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Summary

This study introduces a new method for breast cancer histopathology image classification using assembled Convolutional Neural Networks (CNNs). The approach enhances diagnostic accuracy and reduces pathologist workload, aiding in early breast cancer detection.

Keywords:
Breast cancerChannel pruningHistopathologyHybrid CNN

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Area of Science:

  • Computational pathology
  • Medical image analysis
  • Artificial intelligence in oncology

Background:

  • Breast cancer remains a leading cause of global mortality, necessitating improved diagnostic accuracy and efficiency.
  • Manual histopathology diagnosis is labor-intensive and susceptible to errors, particularly with prolonged pathologist workload.
  • Automated histopathology image recognition is crucial for accelerating diagnosis and enhancing its quality.

Purpose of the Study:

  • To develop an automated system for breast cancer histopathology image classification.
  • To improve diagnostic accuracy and reduce the workload of pathologists.
  • To enhance the generalization ability of classification models.

Main Methods:

  • A hybrid Convolutional Neural Network (CNN) architecture with global and local branches was designed.
  • A Squeeze-Excitation-Pruning (SEP) block was integrated to learn channel importance and remove redundant channels, reducing overfitting.
  • Multiple compact hybrid CNN models were assembled to improve generalization.

Main Results:

  • The proposed hybrid model achieved performance comparable to state-of-the-art methods on the BreaKHis dataset.
  • The multi-model assembling scheme outperformed state-of-the-art methods on the BACH dataset at both patient and image levels.
  • The method demonstrated effectiveness in breast cancer image classification.

Conclusions:

  • A novel compact breast cancer histopathology image classification scheme was developed using assembled hybrid CNNs.
  • The proposed scheme shows promise for auxiliary breast cancer diagnosis, reducing pathologist workload and improving diagnostic quality.
  • This automated approach can significantly aid in early breast cancer detection and treatment planning.