Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition

arXiv:2404.10405v1 Announce Type: cross
Abstract: 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 …

abstract annotated data arxiv byol challenges cs.ai cs.cv cs.lg data image image recognition integration medical paper recognition self-supervised learning semi-supervised semi-supervised learning supervised learning type