Summary of Integration Of Self-supervised Byol in Semi-supervised Medical Image Recognition, by Hao Feng et al.
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition
by Hao Feng, Yuanzhe Jia, Ruijia Xu, Mukesh Prasad, Ali Anaissi, Ali Braytee
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative self-supervised learning approach to enhance medical image recognition when labeled data is limited. Building upon semi-supervised learning techniques, the authors integrate BYOL (Bootstrap Your Own Latent) pre-training with pseudo-labeled and labeled datasets to develop a neural network classifier. The methodology begins by pre-training on unlabeled data using BYOL, followed by iterative fine-tuning to refine the model. Experimental results on three medical image recognition datasets demonstrate that this approach optimizes the use of unlabeled data, outperforming existing methods in terms of accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how computers recognize medical images when there aren’t many labeled examples. Right now, computers rely heavily on labeled data to recognize images, but getting that data can be challenging. The researchers developed a new way to use unlabeled data and some labeled data together to improve image recognition. They tested their approach on three different datasets and found it worked better than other methods. |
Keywords
» Artificial intelligence » Fine tuning » Neural network » Self supervised » Semi supervised
