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

Abstract:

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.

Applications:

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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