Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition
<|reference_start|>Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition: 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.<|reference_end|>
@article{feng2024integration,
    title={Integration of Self-Supervised BYOL in Semi-Supervised Medical Image
  Recognition},
    author={Hao Feng, Yuanzhe Jia, Ruijia Xu, Mukesh Prasad, Ali Anaissi, Ali
  Braytee},
    journal={arXiv preprint arXiv:2404.10405},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10405},
    primaryClass={cs.CV cs.AI cs.LG}
}