Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset

• We employ DPN to learn texture feature representation for small ultrasound dataset.
• We propose the stacked DPN (S-DPN) algorithm for representation learning.
• We apply S-DPN to the ultrasound-based tumor classification task.
• S-DPN can significantly improve representation performance for small ultrasound dataset.

Ultrasound imaging has been widely used for tumor detection and diagnosis. In ultrasound based computer-aided diagnosis, feature representation is a crucial step. In recent years, deep learning (DL) has achieved great success in feature representation learning. However, it generally suffers from the small sample size problem. Since the medical datasets usually have small training samples, texture features are still very commonly used for small ultrasound image datasets. Compared with the commonly used DL algorithms, the newly proposed deep polynomial network (DPN) algorithm not only shows superior performance on large scale data, but also has the potential to learn effective feature representation from a relatively small dataset. In this work, a stacked DPN (S-DPN) algorithm is proposed to further improve the representation performance of the original DPN, and S-DPN is then applied to the task of texture feature learning for ultrasound based tumor classification with small dataset. The task tumor classification is performed on two image dataset, namely the breast B-mode ultrasound dataset and prostate ultrasound elastography dataset. In both cases, experimental results show that S-DPN achieves the best performance with classification accuracies of 92.40±1.1% and 90.28±2.78% on breast and prostate ultrasound datasets, respectively. This level of accuracy is significantly superior to all other compared algorithms in this work, including stacked auto-encoder and deep belief network. It suggests that S-DPN can be a strong candidate for the texture feature representation learning on small ultrasound datasets.