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

Author:

Huang Ruobing,Lin Zehui,Dou Haoran,Wang Jian,Miao Juzheng,Zhou Guangquan,Jia Xiaohong,Xu Wenwen,Mei Zihan,Dong Yijie,Yang Xin,Zhou Jianqiao,Ni Dong

Funder

Guangdong Medical Research Foundation

National Key Research and Development Program of China

Shenzhen Peacock Plan

National Key Research and Development Program of China Stem Cell and Translational Research

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference45 articles.

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2. An enas based approach for constructing deep learning models for breast cancer recognition from ultrasound images;Ahmed;arXiv preprint arXiv:2005.13695,2020

3. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets;Antropova;Med. Phys.,2017

4. Breast lesions: quantitative elastography with supersonic shear imagingpreliminary results;Athanasiou;Radiology,2010

5. Differentiating benign from malignant solid breast masses with us strain imaging;Burnside;Radiology,2007

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