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Dual-branch hybrid encoding embedded network for histopathology image classification.

Mingshuai Li1, Zhiqiu Hu2,3,4, Song Qiu1,5

  • 1Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, People's Republic of China.

Physics in Medicine and Biology
|August 30, 2023
PubMed
Summary

This study introduces a novel dual-branch hybrid network for histopathology image classification, achieving 99.09% accuracy on hepatocellular carcinoma data. The method effectively addresses inter-domain differences for improved multi-cancer classification.

Keywords:
classificationhepatocellular carcinomahistopathological imagehybrid architecture

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Area of Science:

  • Digital pathology
  • Medical image analysis
  • Computational oncology

Background:

  • Histopathology image (HI) classification aids cancer diagnosis but existing methods struggle with inter-domain differences across cancer types.
  • Current approaches are often limited to single cancer types, restricting their broad clinical applicability.

Purpose of the Study:

  • To develop a high-performance histopathology image classification method that overcomes inter-domain differences.
  • To provide a robust and versatile solution for reliable HI classification across multiple cancer types.

Main Methods:

  • A novel dual-branch hybrid encoding embedded network integrating Convolutional Neural Network (CNN) and Transformer for feature extraction.
  • Development of a salient area constraint loss function to address inter-domain differences in HIs.
  • Validation using a new hepatocellular carcinoma (HCC) dataset and two public datasets.

Main Results:

  • Achieved 99.09% accuracy on the proposed HCC dataset.
  • Demonstrated state-of-the-art performance on two additional public histopathology image datasets.
  • The method shows superior performance and versatility in multi-cancer classification.

Conclusions:

  • The proposed method offers a reliable and practical solution for classifying histopathology images across different cancer types.
  • This advancement has the potential to enhance diagnostic accuracy and improve patient outcomes in oncology.
  • Code is publicly available for further research and application.