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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&#x0025; labeled data. It gets precision, recall, F1-score, and accuracy of 98.9&#x0025; on LC25000 and 94.3&#x0025;, 94.5&#x0025;, 94.3&#x0025;, and 94.1&#x0025; on BCCD, respectively. These results outperform other supervised-learning methods using 100&#x0025; labeled data.  Conclusions : The UKSSL can efficiently extract underlying knowledge from the unlabeled dataset and perform better using limited labeled medical images.

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