A Systematic Review: The Application of Attention Mechanisms in Medical Ultrasound Image Processing

Among numerous medical imaging modalities, ultrasound imaging is one of the most commonly used diagnostic methods in clinical practice. However, ultrasound diagnosis heavily relies on physician experience, and diagnostic results often lack reproducibility. In recent years, with the rapid development of artificial intelligence technology, which provides new impetus for the automated medical ultrasound image processing and analysis. Among the numerous deep learning approaches proposed, attention mechanisms have become a key component for improving network robustness to cope with challenges in ultrasound imaging, such as low contrast, blurred boundaries, and variable object morphologies. This paper systematically reviews the attention mechanisms employed in medical ultrasound image analysis, which can be roughly divided these mechanisms into three categories based on the differences in feature focus dimensions: channel attention, spatial attention, and hybrid attention. Most importantly, we not only summarized the application scenarios and effectiveness of various attention mechanisms but also analyzed the potential challenges faced in the future.