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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...
In this paper, a Stacked Sparse Autoencoder (SSAE) based framework is presented for nuclei classification on breast cancer histopathology. SSAE works very well in learning useful high-level feature for better representation of input raw data. To show the effectiveness of proposed framework, SSAE+Softmax is compared with conventional Softmax classifier, PCA+Softmax, and single layer Sparse Autoencoder...
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