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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 …