Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs

Understand

Large numbers of labeled medical images are essential for the accurate detection of anomalies, but manual annotation is labor-intensive and time-consuming.

• Self-supervised learning (SSL) is a training method to learn data-specific features without manual annotation.
• Several SSL-based models have been employed in medical image anomaly detection.
• These SSL methods effectively learn representations in several field-specific images, such as natural and industrial product images.