The AI-based assisted diagnosis programs have been widely investigated on
medical ultrasound images. Complex scenario of ultrasound image, in which the
coupled interference of internal and external factors is severe, brings a
unique challenge for localize the object region automatically and precisely in
ultrasound images. In this study, we seek to propose a more general and robust
Benchmark Attention Adaptive Framework (BAAF) to assist doctors segment or
diagnose lesions and tissues in ultrasound images more quickly and accurately.
Different from existing attention schemes, the BAAF consists of a parallel
hybrid attention module (PHAM) and an adaptive calibration mechanism (ACM).
Specifically, BAAF first coarsely calibrates the input features from the
channel and spatial dimensions, and then adaptively selects more robust lesion
or tissue characterizations from the coarse-calibrated feature maps. The design
of BAAF further optimizes the "what" and "where" focus and selection problems
in CNNs and seeks to improve the segmentation accuracy of lesions or tissues in
medical ultrasound images. The method is evaluated on four medical ultrasound
segmentation tasks, and the adequate experimental results demonstrate the
remarkable performance improvement over existing state-of-the-art methods. In
addition, the comparison with existing attention mechanisms also demonstrates
the superiority of BAAF. This work provides the possibility for automated
medical ultrasound assisted diagnosis and reduces reliance on human accuracy
and precision.

通过提出更普遍和稳健的基准关注自适应框架 (BAAF) 协助医生更快更准确地在超声图像中分割和诊断病变和组织，本研究在四个医学超声分割任务上评估了该方法，充分实验结果显示了与现有先进方法相比明显的性能改善。此外，与现有关注机制的比较也证明了 BAAF 的优越性，为自动化医学超声辅助诊断提供了可能性，减少对人类的精确性和准确性的依赖。