Ultrasound Image Analysis with Vision Transformers-Review

Area of Science:

• Medical imaging
• Artificial intelligence
• Machine learning

Background:

• Ultrasound (US) imaging is crucial in clinical practice but faces challenges like low image quality and high variability.
• Advanced automatic US image analysis methods are needed to improve diagnostic accuracy and objectivity.
• Vision transformers (ViTs) show promise for complex pattern recognition in large datasets.

Purpose of the Study:

• To introduce vision transformers (ViTs) and their application in automatic ultrasound image analysis.
• To review the suitability of ViTs for US image classification, detection, and segmentation tasks.
• To discuss current challenges and future trends of ViTs in medical US image analysis.

Main Methods:

• Review of existing literature on vision transformers and their application to medical ultrasound imaging.
• Analysis of ViT capabilities in handling large datasets and complex patterns relevant to US images.
• Identification of specific US image analysis tasks where ViTs are applied, including classification, detection, and segmentation.

Main Results:

• Vision transformers demonstrate significant potential in enhancing the accuracy and efficiency of US image analysis.
• ViTs are suitable for various automatic US image analysis tasks, including classification, detection, and segmentation.
• The application of ViTs in medical US image analysis is an evolving field with promising future trends.

Conclusions:

• Vision transformers offer a powerful approach to overcome limitations in traditional US image analysis.
• ViTs are expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound.
• Further research into ViTs will drive advancements in objective and accurate medical US image interpretation.