Cited by 240Ultrasound Imaging and ElastographyAI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Deep learning based classification of breast tumors with shear-wave elastography

Zhang, Qi, Xiao, Yang, Dai, Wei, Suo, Jingfeng, Wang, Congzhi, Shi, Jun, Zheng, Hairong·2016·Ultrasonics

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.

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Crossref · 10.1016/j.ultras.2016.08.004

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  • SDAE, PGBM combined with RBM, and GoogLeNet CNN applied to breast US or shear-wave elastography images for breast cancer diagnosis obtained superior performance compared with human experts.

    “In addition, the stacked denoising auto-encoder (SDAE) [88], a combination of the point-wise gated Boltzmann machine (PGBM) and RBM [89], and the GoogLeNet CNN [90] have been applied to breast US or shear-wave elastography images for breast cancer diagnosis; all of these obtained a superior performance when compared with human experts.”

    From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [89]

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Authors
Zhang, Q., Xiao, Y., Dai, W., Suo, J., Wang, C., Shi, J., & Zheng, H.
Affiliations
  • Shanghai University (CN)
  • Chinese Academy of Sciences (CN)
Corresponding author
  • Zhang, Qi
  • Zheng, Hairong
Venue
Ultrasonics
Publisher
Elsevier BV
Published
2016-12
Volume
72
Pages
150-157
ISSN
0041-624X (ISSN-L)
OpenAlex
W2509685700
Cited by
240
References
52
Citations by year
2026: 5 · 2025: 15 · 2024: 35 · 2023: 22 · 2022: 19 · 2021: 40 · 2020: 36 · 2019: 28 · 2018: 19 · 2017: 21
FWCI
19.68
Type
journal article
Language
en
Open access
closed
Locations
  • Ultrasonics (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Natural Science Foundation of China (61401267, 61302039, 61471231)
  • Shanghai Natural Science Fund for Youth Scholars (12ZR1444100)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
breast cancer, shear wave elastography, benign versus malignant breast tumors, deep learning, restricted Boltzmann machine, feature extraction, feature selection, computer-aided diagnosis, tumor classification
MeSH terms
Adult, Biopsy, Breast Neoplasms, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Retrospective Studies, Sensitivity and Specificity, Ultrasonography, Mammary, Neural Networks, Computer, Elasticity Imaging Techniques

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