Multiple Attention-guided Mechanisms for Ultrasound Breast Cancer Tumor Image Segmentation
There are some problems such as single prediction scale and information loss in traditional U-Net ultrasound breast image segmentation tasks.To solve these problems,a multi-attention-guided U-Net ultrasound image segmentation method for breast tumors is proposed.Firstly,multiple SE attention module are introduced into the encoding structure of U-Net to extract multi-level semantic information from the input breast tumor images,which guides the encoder to focus on the features of breast tumor and reduces the interference caused by redundant background information.Secondly,by designing a feature fusion process-ing module,the complex semantic feature fusion processing is carried out on the feature graph from the encoder.Finally,in the de-coder part,the pyramid structure is added to capture global spatial information to improve the multi-scale feature extraction abili-ty of the model for tumor images,so as to improve the expression ability and segmentation performance of the whole network.The proposed method is simulated on breast tumor image data set,and the results show that compared with other U-Net im-proved strategies,the proposed method has better accuracy and robustness.
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