Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset
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Liu et al. and Shi et al. employed deep polynomial network or stacked DPN on two small US datasets and obtained highest classification accuracies of 92.4%, outperforming unsupervised deep learning algorithms such as stacked AE and DBN.
“Both Liu et al. [87] and Shi et al. [19] employed a supervised deep learning algorithm called a deep polynomial network (DPN), or its stacked version, namely stacked DPN (S-DPN), on two small US datasets.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [19]
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Self-citation
- Authors
- Shi, J., Zhou, S., Liu, X., Zhang, Q., Lu, M., & Wang, T.
- Affiliations
- Shanghai University (CN)
- Shanghai Medical College of Fudan University (CN)
- Shenzhen University (CN)
- Corresponding author
- Lu, Minhua
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 194
- Pages
- 87-94
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2288062769
- Cited by
- 166
- References
- 40
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 10 · 2023: 12 · 2022: 15 · 2021: 33 · 2020: 19 · 2019: 20 · 2018: 24 · 2017: 21 · 2016: 6
- FWCI
- 19.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61471231, 61471243, 61401267)
- Innovation Program of Shanghai Municipal Education Commission (13YZ016)
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging
- Shenzhen Project (SGLH20131010163759789, JCYJ20150731160834611)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- feature representation learning, tumor classification, ultrasonography, small sample size problem, computer-aided diagnosis, stacked autoencoder, deep belief network
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