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.