Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition
This work addresses the challenge of obtaining labeled data for medical image recognition, though it appears incremental by combining existing techniques.
The paper tackled the problem of limited labeled data in medical image recognition by integrating self-supervised BYOL pre-training with semi-supervised learning, resulting in improved accuracy on three datasets compared to existing methods.
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.