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Related Experiment Video

Updated: Jul 30, 2025

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A multi-view co-training network for semi-supervised medical image-based prognostic prediction.

Hailin Li1, Siwen Wang2, Bo Liu3

  • 1Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 14, 2023
PubMed
Summary

This study introduces Co-DeepSVS, a novel semi-supervised deep learning model for improved prognostic prediction using censored medical data. The model enhances the efficiency of utilizing censored data for more accurate survival time estimation.

Keywords:
Deep neural networkMedical image analysisPrognostic predictionSemi-supervised learning

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Area of Science:

  • Medical imaging analysis
  • Machine learning in healthcare
  • Prognostic modeling

Background:

  • Prognostic prediction is crucial for personalized treatment strategies.
  • Censored observations present a significant challenge in survival analysis.
  • Semi-supervised learning offers potential for improved utilization of censored data.

Purpose of the Study:

  • To develop an effective semi-supervised learning paradigm for prognostic prediction.
  • To improve the efficiency of utilizing censored data in prognostic models.
  • To introduce a novel deep neural network for survival time estimation.

Main Methods:

  • Proposed a semi-supervised co-training deep neural network (Co-DeepSVS) with a support vector regression (SVR) layer.
  • Integrated SVR layer to handle censored data and predict survival time, calculating labeling confidence.
  • Applied a semi-supervised multi-view co-training framework with pseudo-time based confidence estimation.

Main Results:

  • Co-DeepSVS demonstrated promising prognostic ability on a multi-phase CT dataset.
  • The proposed model outperformed widely used prognostic prediction methods.
  • The SVR layer enhanced model robustness against follow-up bias.

Conclusions:

  • Co-DeepSVS effectively improves the utilization of censored data for prognostic prediction.
  • The integration of SVR layer enhances the reliability of deep learning models in survival analysis.
  • This approach holds significant clinical implications for personalized medicine.