Medical image classification based on semi-supervised generative adversarial network and pseudo-labelling

Kun Liu, Xiaolin Ning, Sidong Liu*

*Corresponding author for this work

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Deep learning has substantially improved the state-of-the-art in object detection and image classification. Deep learning usually requires large-scale labelled datasets to train the models; however, due to the restrictions in medical data sharing and accessibility and the expensive labelling cost, the application of deep learning in medical image classification has been dramatically hindered. In this study, we propose a novel method that leverages semi-supervised adversarial learning and pseudo-labelling to incorporate the unlabelled images in model learning. We validate the proposed method on two public databases, including ChestX-ray14 for lung disease classification and BreakHis for breast cancer histopathological image diagnosis. The results show that our method achieved highly effective performance with an accuracy of 93.15% while using only 30% of the labelled samples, which is comparable to the state-of-the-art accuracy for chest X-ray classification; it also outperformed the current methods in multi-class breast cancer histopathological image classification with a high accuracy of 96.87%.
Original languageEnglish
Article number9967
Pages (from-to)1-12
Number of pages12
Issue number24
Publication statusPublished - 17 Dec 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • digital histopathology
  • deep learning
  • generative adversarial network
  • k-means clustering
  • medical images classification
  • semi-supervised learning


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