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Related Experiment Video

Updated: Nov 1, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data.

Yawen Xiao1, Jun Wu2, Zongli Lin3

  • 1Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China.

Computers in Biology and Medicine
|June 21, 2021
PubMed
Summary

This study introduces an improved Wasserstein generative adversarial network (WGAN) to address imbalanced cancer gene expression data. The WGAN method enhances cancer diagnosis accuracy by generating synthetic data for underrepresented classes.

Keywords:
Cancer diagnosisDeep learningGene expression dataImbalanced dataWasserstein generative adversarial networks

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cancer diagnosis relies heavily on accurate classification of gene expression data.
  • Clinical data often suffers from imbalance, where certain cancer types are underrepresented, hindering traditional diagnostic models.
  • Addressing data imbalance is crucial for improving the predictive performance of cancer diagnostic tools.

Purpose of the Study:

  • To develop and evaluate an improved deep learning model for handling imbalanced cancer gene expression data.
  • To enhance the accuracy of cancer diagnosis by mitigating the effects of data scarcity in minority classes.
  • To explore the utility of Wasserstein generative adversarial networks (WGANs) for data-level solutions to imbalanced learning problems in oncology.

Main Methods:

  • An improved deep learning Wasserstein generative adversarial network (WGAN) model was developed to address data imbalance.
  • The WGAN model was utilized to generate synthetic gene expression samples for minority classes, rebalancing the dataset.
  • The proposed WGAN approach was applied to three publicly available RNA-seq datasets from different cancer types.

Main Results:

  • The WGAN model effectively generated new samples for underrepresented cancer gene expression data.
  • Application of the WGAN resulted in balanced data distributions and increased sample sizes across all analyzed datasets.
  • The WGAN method led to significant improvements in prediction accuracy for cancer diagnosis compared to conventional sampling methods.

Conclusions:

  • The proposed WGAN method demonstrates superior performance in addressing imbalanced learning for cancer gene expression data.
  • This approach offers a robust data-level solution for imbalanced datasets, enhancing cancer diagnostic accuracy.
  • The WGAN provides a reliable tool for improving predictive models in cancer research and clinical applications.