POPAR: Patch Order Prediction and Appearance Recovery for self-supervised learning in chest radiography

Area of Science:

• Computer Vision
• Medical Imaging Analysis
• Machine Learning

Background:

• Self-supervised learning (SSL) excels in computer vision but faces challenges in medical imaging due to data differences.
• Large annotated datasets are often scarce in medical fields, hindering traditional supervised approaches.

Purpose of the Study:

• To introduce POPAR (Patch Order Prediction and Appearance Recovery), a novel SSL framework for medical image analysis.
• To enhance chest X-ray interpretation using SSL by addressing domain-specific challenges.

Main Methods:

• POPAR employs two strategies: patch order prediction for spatial/anatomical learning and patch appearance recovery for texture details.
• A Swin Transformer backbone is utilized for pretraining on a large-scale dataset.

Main Results:

• POPAR outperforms existing SSL and fully supervised models in classification, segmentation, and anatomical understanding.
• The framework demonstrates improved bias robustness and data efficiency in medical imaging tasks.

Conclusions:

• POPAR offers a scalable and effective SSL solution for medical imaging, particularly chest X-rays.
• The approach shows strong generalization capabilities and reduces reliance on extensive annotations.