AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images
This work addresses the problem of accurate breast lesion segmentation in medical imaging for improved diagnosis, though it appears incremental as it builds upon existing U-net and attention mechanisms.
The authors tackled the challenge of segmenting breast lesions in ultrasound images, which is difficult due to similar intensities, variable morphology, and blurred boundaries, by developing an adaptive attention U-net (AAU-net) that achieved better performance and generalization compared to state-of-the-art methods on three public datasets.
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.