B-mode ultrasound based CAD for liver cancers via multi-view privileged information learning

Xiangmin Han; Bangming Gong; Lehang Guo; Jun Wang; Shihui Ying; Shuo Li; Jun Shi

doi:10.1016/j.neunet.2023.03.028

B-mode ultrasound based CAD for liver cancers via multi-view privileged information learning

Neural Netw. 2023 Jul:164:369-381. doi: 10.1016/j.neunet.2023.03.028. Epub 2023 Mar 25.

Authors

Xiangmin Han¹, Bangming Gong¹, Lehang Guo², Jun Wang¹, Shihui Ying³, Shuo Li⁴, Jun Shi⁵

Affiliations

¹ Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information Engineering, Shanghai University, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, China.
² Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai, China. Electronic address: gopp1314@hotmail.com.
³ Department of Mathematics, School of Science, Shanghai University, China.
⁴ The Digital Imaging Group of London, The Department of Medical Imaging, Western University, London, Canada.
⁵ Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Communication and Information Engineering, Shanghai University, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, China. Electronic address: junshi@shu.edu.cn.

PMID: 37167750
DOI: 10.1016/j.neunet.2023.03.028

Abstract

B-mode ultrasound-based computer-aided diagnosis model can help sonologists improve the diagnostic performance for liver cancers, but it generally suffers from the bottleneck due to the limited structure and internal echogenicity information in B-mode ultrasound images. Contrast-enhanced ultrasound images provide additional diagnostic information on dynamic blood perfusion of liver lesions for B-mode ultrasound images with improved diagnostic accuracy. Since transfer learning has indicated its effectiveness in promoting the performance of target computer-aided diagnosis model by transferring knowledge from related imaging modalities, a multi-view privileged information learning framework is proposed to improve the diagnostic accuracy of the single-modal B-mode ultrasound-based diagnosis for liver cancers. This framework can make full use of the shared label information between the paired B-mode ultrasound images and contrast-enhanced ultrasound images to guide knowledge transfer It consists of a novel supervised dual-view deep Boltzmann machine and a new deep multi-view SVM algorithm. The former is developed to implement knowledge transfer from the multi-phase contrast-enhanced ultrasound images to the B-mode ultrasound-based diagnosis model via a feature-level learning using privileged information paradigm, which is totally different from the existing learning using privileged information paradigm that performs knowledge transfer in the classifier. The latter further fuses and enhances feature representation learned from three pre-trained supervised dual-view deep Boltzmann machine networks for the classification task. An experiment is conducted on a bimodal ultrasound liver cancer dataset. The experimental results show that the proposed framework outperforms all the compared algorithms with the best classification accuracy of 88.91 ± 1.52%, sensitivity of 88.31 ± 2.02%, and specificity of 89.50 ± 3.12%. It suggests the effectiveness of our proposed MPIL framework for the BUS-based CAD of liver cancers.

Keywords: B-mode ultrasound; Contrast-enhanced ultrasound; Feature-level knowledge transfer; Multi-view privileged information learning.

MeSH terms

Algorithms
Diagnosis, Computer-Assisted
Humans
Liver Neoplasms* / diagnostic imaging
Ultrasonography / methods