UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification

Zeyu Ren, Xiangyu Kong, Yudong Zhang*, Shuihua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Goal: Deep learning techniques have made significant progress in medical image analysis. However, obtaining ground truth labels for unlabeled medical images is challenging as they often outnumber labeled images. Thus, training a high-performance model with limited labeled data has become a crucial challenge. Methods: This study introduces an underlying knowledge-based semi-supervised framework called UKSSL, consisting of two components: MedCLR extracts feature representations from the unlabeled dataset; UKMLP utilizes the representation and fine-tunes it with the limited labeled dataset to classify the medical images. Results: UKSSL evaluates on the LC25000 and BCCD datasets, using only 50% labeled data. It gets precision, recall, F1-score, and accuracy of 98.9% on LC25000 and 94.3%, 94.5%, 94.3%, and 94.1% on BCCD, respectively. These results outperform other supervised-learning methods using 100% labeled data. Conclusions: The UKSSL can efficiently extract underlying knowledge from the unlabeled dataset and perform better using limited labeled medical images.

Original languageEnglish
Pages (from-to)459-466
Number of pages8
JournalIEEE Open Journal of Engineering in Medicine and Biology
Publication statusPublished - 2024
Externally publishedYes


  • Deep learning
  • image classification
  • medical image analysis
  • self-supervised learning
  • semi-supervised learning


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