BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks

TL;DR

The paper introduces BAAF, a novel attention framework with hybrid attention and calibration mechanisms, significantly improving ultrasound image segmentation accuracy and robustness over existing methods.

Contribution

It proposes a general, robust attention framework with hybrid attention and adaptive calibration for better ultrasound image segmentation.

Findings

BAAF outperforms state-of-the-art methods in four ultrasound segmentation tasks.

The hybrid attention and calibration mechanisms enhance focus on relevant features.

Experimental results demonstrate BAAF's superior accuracy and robustness.

Abstract

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.