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.

翻译：图像识别技术高度依赖于充足的标注数据，尤其是在医学领域。为了解决标注数据获取的挑战，自监督学习和半监督学习在标注数据有限的场景中变得尤为突出。本文提出了一种创新方法，将自监督学习融入半监督模型以提升医学图像识别性能。该方法首先利用BYOL方法在未标注数据上进行预训练，随后合并伪标注数据集与标注数据集构建神经网络分类器，并通过迭代微调进行优化。在三个不同数据集上的实验结果表明，该方法能够最优地利用未标注数据，在医学图像识别的准确率上优于现有方法。