title: Deep learning based classification of breast tumors with shear-wave elastography.
abstract: This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer.
authors.affiliation: School of Communication and Information Engineering, Shanghai University, Shanghai, China. Electronic address: zhangq@shu.edu.cn.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiao
authors.affiliation: School of Communication and Information Engineering, Shanghai University, Shanghai, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Dai
authors.affiliation: School of Communication and Information Engineering, Shanghai University, Shanghai, China.
authors.forename: Jingfeng
authors.initials: J
authors.lastname: Suo
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Congzhi
authors.initials: C
authors.lastname: Wang
authors.affiliation: School of Communication and Information Engineering, Shanghai University, Shanghai, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Shi
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Electronic address: hr.zheng@siat.ac.cn.
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
mesh_terms: D000328:Adult; D001706:Biopsy; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D003937:Diagnosis, Differential; D054459:Elasticity Imaging Techniques / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*
keywords: Breast tumors; Computer-aided diagnosis; Deep learning; Point-wise gated Boltzmann machine; Shear-wave elastography