AW3M: An auto-weighting and recovery framework for breast cancer diagnosis using multi-modal ultrasound.

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作者： Ruobing,Huang ^[1] ; Zehui,Lin ^[1] ; Haoran,Dou ^[1] ; Jian,Wang ^[1] ; Juzheng,Miao ^[2] ; Guangquan,Zhou ^[2] ; Xiaohong,Jia ^[3] ; Wenwen,Xu ^[3] ; Zihan,Mei ^[3] ; Yijie,Dong ^[3] ; Xin,Yang ^[1] ; Jianqiao,Zhou ^[4] ; Dong,Ni ^[5]

作者单位： Medical UltraSound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China. ^[1] School of Biological Sciences and Medical Engineering, Southeast University, China. ^[2] Department of Ultrasound Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, China. ^[3] Department of Ultrasound Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, China. Electronic address: zhousu30@126.com. ^[4] Medical UltraSound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China. Electronic address: nidong@szu.edu.cn. ^[5]

关键词 Breast cancer Missing data Multi-modality Reinforcement learning Ultrasound

主题词乳腺肿瘤(Breast Neoplasms);诊断, 计算机辅助(Diagnosis, Computer-Assisted);弹性成像技术(Elasticity Imaging Techniques);女(雌)性(Female);人类(Humans);超声检查(Ultrasonography);超声检查, 乳房(Ultrasonography, Mammary)

DOI 10.1016/j.media.2021.102137

PMID 34216958

发布时间 2021-08-02

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