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A semi-supervised learning framework for micropapillary adenocarcinoma detection

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Micropapillary adenocarcinoma is a distinctive histological subtype of lung adenocarcinoma with poor prognosis. Computer-aided diagnosis method has the potential to provide help for its early diagnosis. But the implementation of the existing methods largely relies on massive manually labeled data and consumes a lot of time and energy. To tackle these problems, we propose a framework that applies semi-supervised learning method to detect micropapillary adenocarcinoma, which aims to utilize labeled and unlabeled data better.

Methods

The framework consists of a teacher model and a student model. The teacher model is first obtained by using the labeled data. Then, it makes predictions on unlabeled data as pseudo-labels for students. Finally, high-quality pseudo-labels are selected and associated with the labeled data to train the student model. During the learning process of the student model, augmentation is added so that the student model generalizes better than the teacher model.

Results

Experiments are conducted on our own whole slide micropapillary lung adenocarcinoma histopathology image dataset and we selected 3527 patches for the experiment. In the supervised learning, our detector achieves a precision of 0.762 and recall of 0.884. In the semi-supervised learning, our method achieves a precision of 0.775 and recall of 0.896; it is superior to other methods.

Conclusion

We proposed a semi-supervised learning framework for micropapillary adenocarcinoma detection, which has better performance in utilizing both labeled and unlabeled data. In addition, the detector we designed improves the detection accuracy and speed and achieves promising results in detecting micropapillary adenocarcinoma.

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Funding

This study was funded by the Primary Research & Development Plan of Shandong Province (No.2017GGX10112), the Natural Science Foundation of Shandong Province (ZR2020MF051), National Natural Science Foundation of China (NO.81871508, NO.61773246,NO.61572300).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, People’s Republic of China

    Yuan Gao, Yanhui Ding, Xiaodan Sui, Yanna Zhao & Yuanjie Zheng

  2. Shandong Provincial Hospital, Jinan, 250013, People’s Republic of China

    Wei Xiao, Zhigang Yao & Xiaoming Zhou

Authors
  1. Yuan Gao
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  6. Xiaodan Sui
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  7. Yanna Zhao
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  8. Yuanjie Zheng
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Correspondence to Yanhui Ding or Yanna Zhao.

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Gao, Y., Ding, Y., Xiao, W. et al. A semi-supervised learning framework for micropapillary adenocarcinoma detection. Int J CARS 17, 639–648 (2022). https://doi.org/10.1007/s11548-022-02565-8

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  • DOI: https://doi.org/10.1007/s11548-022-02565-8

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