HomeProjectsBiomedical Sciences and Healthcare TechnologiesAutomatic Analysis of Breast Cancer Histology Images

Automatic Analysis of Breast Cancer Histology Images

Project Quick Facts

Principal Investigator

  • Prof. HENG Pheng Ann

    Department of Computer Science and Engineering

  • Funding

    Innovation and Technology Commission


The detection of lymph node metastasis gives an important aggressiveness indication of the invasive breast cancer. However, visual inspection relying on pathologists is expensive and time-consuming. We propose a fast and robust method to detect mitosis by designing a novel deep cascaded convolutional neural network.

Problem to be solved:

Automatic detection of lymph node metastasis histopathological images for breast cancer diagnosis.


Cancer diagnosis via histopathological image.

Metastasis detection of breast cancer histology images with cascaded convolutional neural networks

Uniqueness and Competitive Advantages:

The deep cascaded neural network is composed of two components. First, by leveraging the fully convolutional network, we propose a coarse retrieval model to identify and locate the candidates of mitosis while preserving a high sensitivity. Based on these candidates, a fine discrimination model is developed to further single out lymph node metastasis.

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