BAAF: A Benchmark Attention Adaptive Framework for Medical Ultrasound
  Image Segmentation Tasks
Gongping Chen Lei Zhao Xiaotao Yin Liang Cui Jianxun Zhang Yu Dai
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.