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Breast cancer cell nuclei classification in histopathology images using deep neural networks

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Cell nuclei classification in breast cancer histopathology images plays an important role in effective diagnose since breast cancer can often be characterized by its expression in cell nuclei. However, due to the small and variant sizes of cell nuclei, and heavy noise in histopathology images, traditional machine learning methods cannot achieve desirable recognition accuracy. To address this challenge, this paper aims to present a novel deep neural network which performs representation learning and cell nuclei recognition in an end-to-end manner.

Methods

The proposed model hierarchically maps raw medical images into a latent space in which robustness is achieved by employing a stacked denoising autoencoder. A supervised classifier is further developed to improve the discrimination of the model by maximizing inter-subject separability in the latent space. The proposed method involves a cascade model which jointly learns a set of nonlinear mappings and a classifier from the given raw medical images. Such an on-the-shelf learning strategy makes obtaining discriminative features possible, thus leading to better recognition performance.

Results

Extensive experiments with benign and malignant breast cancer datasets are conducted to verify the effectiveness of the proposed method. Better performance was obtained when compared with other feature extraction methods, and higher recognition rate was achieved when compared with other seven classification methods.

Conclusions

We propose an end-to-end DNN model for cell nuclei and non-nuclei classification of histopathology images. It demonstrates that the proposed method can achieve promising performance in cell nuclei classification, and the proposed method is suitable for the cell nuclei classification task.

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Notes

  1. The BCC database is downloaded from the website at http://bioimage.ucsb.edu/research/bio-segmentation.

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Acknowledgements

This work is supported by the Fok Ying Tung Education Foundation under Grant 151068, the Foundation for Youth Science and Technology Innovation Research Team of Sichuan Province 2016TD0018, and the National Natural Science Foundation of China under Grant 61332002.

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China

    Yangqin Feng, Lei Zhang & Zhang Yi

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  1. Yangqin Feng
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  2. Lei Zhang
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  3. Zhang Yi
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Correspondence to Lei Zhang.

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No animal or human experiments were conducted as part of this research.

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Feng, Y., Zhang, L. & Yi, Z. Breast cancer cell nuclei classification in histopathology images using deep neural networks. Int J CARS 13, 179–191 (2018). https://doi.org/10.1007/s11548-017-1663-9

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