Submit Manuscript  

Article Details


Ultrasound Image Based Tumor Classification via Deep Polynomial Network and Multiple Kernel Learning

[ Vol. 14 , Issue. 2 ]

Author(s):

Jun Shi, Yiyi Qian, Jinjie Wu, Shichong Zhou*, Yehua Cai*, Qi Zhang, Xiaoxing Feng and Cai Chang   Pages 301 - 308 ( 8 )

Abstract:


Background: Ultrasound imaging is widely used for tumor detection and diagnosis. Feature extraction plays a critical role in the ultrasound-based computer-aided diagnosis system. Deep Polynomial Network (DPN) is a newly proposed deep learning algorithm, which also has the potential to learn for excellent representation from small dataset.

Discussion: However, the final feature representation of DPN is the simple concatenation of the learned hierarchical features from different network layers, which essentially loses some properties exhibited by different network layers, and depresses the representative performance. Since the hierarchical features in DPN can be regarded as heterogeneous multi-view features, they can be effectively integrated by Multiple Kernel Learning (MKL) methods.

Conclusion: In this work, we proposed a DPN and MKL based feature learning and classification framework (DPN-MKL) for ultrasound image based tumor diagnosis. The experimental results on breast ultrasound image dataset and prostate ultrasound image dataset show that DPN algorithm has superior performance to the commonly used deep learning algorithms, while the proposed DPNMKL framework outperforms all the single-view feature based algorithms.

Keywords:

Deep polynomial network, multiple kernel learning, ultrasound image, tumor diagnosis, prostate.

Affiliation:

School of Communication and Information Engineering, Institute of Biomedical Engineering, Shanghai University, Shanghai, School of Communication and Information Engineering, Institute of Biomedical Engineering, Shanghai University, Shanghai, School of Communication and Information Engineering, Institute of Biomedical Engineering, Shanghai University, Shanghai, Department of Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, Department of Ultrasonography, Huashan Hospital, Fudan University, Shanghai, School of Communication and Information Engineering, Institute of Biomedical Engineering, Shanghai University, Shanghai, Shenzhen Micro&Nano Research Institute of IC and System Applications, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Department of Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai

Graphical Abstract:



Read Full-Text article