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

arXiv: https://arxiv.org/abs/2404.10405 · HTML: https://mlsanity.com/paper/2404.10405/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations](https://mlsanity.com/paper/2003.12237/) (2020)
- [Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model](https://mlsanity.com/paper/2005.07377/) (2020)
- [Semi-supervised Medical Image Classification with Global Latent Mixing](https://mlsanity.com/paper/2005.11217/) (2020)
- [SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation](https://mlsanity.com/paper/2301.00366/) (2023)

## Built on by (most cited first, 2 of 2)

- [DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations](https://mlsanity.com/paper/2505.03204/) (2025)
- [Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels](https://mlsanity.com/paper/2610.13221/) (2026)

## Abstract (arXiv)

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.