Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
Open accessAbstract
Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of "Deep Learning" strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.
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- Authors
- Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., & Madabhushi, A.
- Affiliations
- Nanjing University of Information Science and Technology (CN)
- Case Western Reserve University (US)
- Jiangsu Cancer Hospital (CN)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-01
- Volume
- 35
- Issue
- 1
- Pages
- 119-130
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC4729702
- OpenAlex
- W2248620004
- Cited by
- 854
- References
- 44
- Citations by year
- 2026: 18 · 2025: 35 · 2024: 48 · 2023: 98 · 2022: 101 · 2021: 119 · 2020: 118 · 2019: 112 · 2018: 107 · 2017: 74 · 2016: 23
- FWCI
- 66.67
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- DOD Lung Cancer Idea Development New Investigator Award (LC130463)
- DOD Prostate Cancer Synergistic Idea Development Award (PC120857)
- Natural Science Foundation of Jiangsu Province of China (BK20141482)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK098503-02)
- Six Major Talents Summit of Jiangsu Province (2013-XXRJ-019)
- National Institutes of Health (R01CA136535-01, R01CA140772-01, R21CA167811-01, R21CA179327-01)
- National Natural Science Foundation of China (61272223, 61273259)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- stacked sparse autoencoder, nuclei detection, breast cancer, histopathological images, deep learning, automated grading, sliding window classification, representation learning
- MeSH terms
- Machine Learning, Algorithms, Breast Neoplasms, Cell Nucleus, Female, Histocytochemistry, Humans, Image Processing, Computer-Assisted
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