  Image recognition techniques heavily rely on abundant labeled data,
particularly in medical contexts. Addressing the challenges associated with
obtaining labeled data has led to the prominence of self-supervised learning
and semi-supervised learning, especially in scenarios with limited annotated
data. In this paper, we proposed an innovative approach by integrating
self-supervised learning into semi-supervised models to enhance medical image
recognition. Our methodology commences with pre-training on unlabeled data
utilizing the BYOL method. Subsequently, we merge pseudo-labeled and labeled
datasets to construct a neural network classifier, refining it through
iterative fine-tuning. Experimental results on three different datasets
demonstrate that our approach optimally leverages unlabeled data, outperforming
existing methods in terms of accuracy for medical image recognition.
