Feature guided segmentation algorithm of mammograms fusion with self attention
In order to enhance the recognition accuracy of breast cancer mammography,we designed a feature guided attention net-work for the segmentation of breast mass and calcification areas.Firstly,the feature extraction module was designed to learn semantic features of breast tissue.Then,the decoding module integrating self correcting attention was used to enhance attention to the edge infor-mation of the lesion area,and improve the clarity of the boundary.Finally,feature guided attention module was used to enhance chan-nel dependencies,further restore edge details of the lesion area,and improve segmentation accuracy.The experimental results showed that the average Dice coefficient(mDice)of mass and calcification segmentation on the expanded INBreast1 reached 0.971 and 0.888 respectively,the mDice of mass segmentation on DDSM reached 0.911,which was better than that of other conventional segmentation models.The research is of great significance for early diagnosis and treatment of breast cancer.
Breast cancerMammogramsImage segmentationSelf attentionFeature guided
国家科技期刊平台
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