自监督BYOL在半监督医学图像识别中的集成
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition
AI总结：
针对医学图像标注数据稀缺问题，提出将自监督BYOL集成到半监督模型的方法，经无标注预训练、伪标与标注数据合并训练及迭代微调，在三个数据集上准确率优于现有方法。
摘要：
图像识别技术高度依赖大量标注数据，在医学领域尤其如此。为应对标注数据获取困难的挑战，自监督学习与半监督学习逐渐成为研究热点，尤其适用于标注数据有限的场景。本文提出一种创新方法，将自监督学习集成到半监督模型中以提升医学图像识别效果。该方法首先利用BYOL（自举你自己的潜变量表示）方法在无标注数据上进行预训练，随后将伪标注数据集与标注数据集合并构建神经网络分类器，并通过迭代微调对其进行优化。在三个不同数据集上的实验结果表明，该方法能最优利用无标注数据，在医学图像识别准确率上优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
