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A semi-supervised learning framework for micropapillary adenocarcinoma detection.

Yuan Gao1, Yanhui Ding2, Wei Xiao3

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

International Journal of Computer Assisted Radiology and Surgery
|February 12, 2022
PubMed
Summary

This study introduces a semi-supervised learning framework to improve the early detection of micropapillary adenocarcinoma, a lung cancer subtype. The novel method effectively uses both labeled and unlabeled data, enhancing diagnostic accuracy and speed.

Keywords:
Computer-aided diagnosisDeep neural convolution networkLung adenocarcinomaLung adenocarcinoma detectionMicropapillary patternPathological image

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Area of Science:

  • Digital pathology
  • Machine learning in oncology
  • Histopathology image analysis

Background:

  • Micropapillary adenocarcinoma is a lung adenocarcinoma subtype with a poor prognosis.
  • Early diagnosis is crucial but hindered by the need for extensive labeled data in current computer-aided methods.

Purpose of the Study:

  • To develop a semi-supervised learning framework for detecting micropapillary adenocarcinoma.
  • To improve the utilization of both labeled and unlabeled data for more efficient diagnosis.

Main Methods:

  • A teacher-student model framework was implemented.
  • The teacher model, trained on labeled data, generated pseudo-labels for unlabeled data.
  • Pseudo-labels were refined and combined with labeled data to train the student model with added augmentation for better generalization.

Main Results:

  • The semi-supervised method achieved a precision of 0.775 and recall of 0.896 on a dataset of 3527 histopathology image patches.
  • Performance surpassed traditional supervised learning (precision 0.762, recall 0.884) and other existing methods.
  • The developed detector demonstrated improved accuracy and speed in micropapillary adenocarcinoma detection.

Conclusions:

  • The proposed semi-supervised learning framework effectively leverages both labeled and unlabeled data for micropapillary adenocarcinoma detection.
  • The novel detector shows promising results, enhancing diagnostic accuracy and speed for this specific lung cancer subtype.