The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Ultrasound Technology

Background:

• Self-supervised learning (SSL) relies heavily on data augmentation.
• Standard augmentation techniques may not transfer effectively to medical imaging, particularly lung ultrasound.
• Optimizing SSL for medical tasks requires tailored data strategies.

Purpose of the Study:

• To systematically investigate the impact of data augmentation and preprocessing on SSL performance in lung ultrasound.
• To compare different augmentation pipelines, including a novel semantic-preserving approach.
• To provide guidance for SSL implementation in ultrasound imaging.

Main Methods:

• Assessed three data augmentation pipelines: baseline, semantic-preserving for ultrasound, and a distilled set.
• Evaluated pretrained models on B-line detection, pleural effusion detection, and COVID-19 classification tasks.
• Investigated the effect of semantic-preserving ultrasound image preprocessing.

Main Results:

• Semantic-preserving augmentation yielded the best performance for COVID-19 classification, emphasizing global context.
• Cropping-based augmentation excelled in B-line and pleural effusion detection, requiring local pattern recognition.
• Semantic-preserving preprocessing consistently improved downstream task performance.

Conclusions:

• Tailored data augmentation and preprocessing are crucial for effective SSL in lung ultrasound.
• The choice of augmentation strategy should align with the specific diagnostic task's requirements (local vs. global context).
• Findings offer practical guidance for researchers and developers using SSL for ultrasound analysis.