AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images
Gongping Chen, Yu Dai, Jianxun Zhang, Moi Hoon Yap
Various deep learning methods have been proposed to segment breast lesion
from ultrasound images. However, similar intensity distributions, variable
tumor morphology and blurred boundaries present challenges for breast lesions
segmentation, especially for malignant tumors with irregular shapes.
Considering the complexity of ultrasound images, we develop an adaptive
attention U-net (AAU-net) to segment breast lesions automatically and stably
from ultrasound images. Specifically, we introduce a hybrid adaptive attention
module, which mainly consists of a channel self-attention block and a spatial
self-attention block, to replace the traditional convolution operation.
Compared with the conventional convolution operation, the design of the hybrid
adaptive attention module can help us capture more features under different
receptive fields. Different from existing attention mechanisms, the hybrid
adaptive attention module can guide the network to adaptively select more
robust representation in channel and space dimensions to cope with more complex
breast lesions segmentation. Extensive experiments with several
state-of-the-art deep learning segmentation methods on three public breast
ultrasound datasets show that our method has better performance on breast
lesion segmentation. Furthermore, robustness analysis and external experiments
demonstrate that our proposed AAU-net has better generalization performance on
the segmentation of breast lesions. Moreover, the hybrid adaptive attention
module can be flexibly applied to existing network frameworks.