Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

@article{Sirinukunwattana2016LocalitySD,
  title={Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images},
  author={Korsuk Sirinukunwattana and Shan-e-Ahmed Raza and Yee-Wah Tsang and David R. J. Snead and Ian A. Cree and Nasir M. Rajpoot},
  journal={IEEE Transactions on Medical Imaging},
  year={2016},
  volume={35},
  pages={1196-1206}
}
Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a… CONTINUE READING
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Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

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