Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
Nanjing University of Information Science and Technology · Case Western Reserve University · +1 more institution
Abstract
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,…
Citation impact
- FWCI
- 91.71
- Percentile
- 100%
- References
- 49
Authors
7Topics & keywords
- Autoencoder
- Artificial intelligence
- Computer science
- Digital pathology
- Deep learning
- Pattern recognition (psychology)
- Pixel
- Computer vision
- Good health and well-being