Integration of Self-Supervised BYOL in Semi-Supervised Medical Image
  Recognition
Hao Feng Yuanzhe Jia Ruijia Xu Mukesh Prasad Ali Anaissi Ali Braytee
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.