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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.
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.