AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images
| Autor: | Chen, Gongping, Dai, Yu, Zhang, Jianxun, Yap, Moi Hoon |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Medical Imaging ( Volume: 42, Issue: 5, May 2023) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/TMI.2022.3226268 |
| Popis: | 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. Comment: Breast cancer segmentation, Ultrasound images, Hybrid attention, Adaptive learning, Deep learning |
| Databáze: | arXiv |
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