Improved Cystic Hygroma Detection with Self-Supervised Learning

This paper addresses the challenge of detecting cystic hygroma, a high-risk prenatal condition, using ultrasound images. The key contribution is the application of ultrasound-specific self-supervised learning (USF-MAE) to overcome the limitations of small labeled datasets. The results demonstrate significant improvements over a baseline model, highlighting the potential of this approach for early screening and improved patient outcomes.

• •Self-supervised learning, specifically USF-MAE, is effective for detecting cystic hygroma in ultrasound images.
• •The model achieves high accuracy, sensitivity, and specificity, outperforming a standard baseline.
• •The approach addresses the challenge of limited labeled data in medical imaging.
• •Model interpretability is enhanced through Score-CAM visualizations, showing clinical relevance.