生物医学工程研究2024，Vol.43Issue(1)：55-61,7.DOI:10.19529/j.cnki.1672-6278.2024.01.08
融合自注意力的乳腺钼靶图像特征引导分割算法
Feature guided segmentation algorithm of mammograms fusion with self attention
摘要
Abstract
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.关键词
乳腺癌/钼靶图像/图像分割/自注意力/特征引导Key words
Breast cancer/Mammograms/Image segmentation/Self attention/Feature guided分类
医药卫生引用本文复制引用
申文静,丛金玉,班楷第,王苹苹,刘坤孟,司兴勇,魏本征..融合自注意力的乳腺钼靶图像特征引导分割算法[J].生物医学工程研究,2024,43(1):55-61,7.基金项目
山东省自然科学基金资助项目(ZR2020QF043,ZR2022QG051,ZR2023QF094). （ZR2020QF043,ZR2022QG051,ZR2023QF094）
