Summary of Label-efficient Self-supervised Federated Learning For Tackling Data Heterogeneity in Medical Imaging, by Rui Yan et al.
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
by Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou
First submitted to arxiv on: 17 May 2022
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 a robust and label-efficient self-supervised federated learning (FL) framework for medical image analysis. The authors address the challenges of heterogeneous data distributions and limited labeled data in FL by introducing a novel Transformer-based self-supervised pre-training paradigm, which pre-trains models on decentralized target task datasets using masked image modeling. This approach enables more robust representation learning and effective knowledge transfer to downstream models. Empirical results show that this method significantly improves model robustness against various degrees of data heterogeneity, outperforming existing FL algorithms. The authors demonstrate the effectiveness of their framework by achieving improvements in test accuracy on retinal, dermatology, and chest X-ray classification tasks compared to a supervised baseline with ImageNet pre-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier for different hospitals or research centers to work together on medical image analysis without sharing sensitive patient data. They propose a new way of training AI models that can handle different types of images and improve performance when working together. The approach uses a type of AI called Transformers to pre-train the models before they start learning specific tasks. This helps the models become more robust and accurate, even when the images are very different from what they were trained on. The results show that this method is better than existing approaches and can improve accuracy by up to 5% in certain medical image analysis tasks. |
Keywords
» Artificial intelligence » Classification » Federated learning » Representation learning » Self supervised » Supervised » Transformer
