基于深度级联网络的乳腺淋巴结全景图像的癌转移区域自动识别

doi:10.3969/j.issn.0258-8021.2020.03.01

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Abstract Automated recognition of the cancer metastasis region in lymph nodes is an essential prerequisite for the pathological staging of breast cancer. However, due to the massive size of panoramic images and the complexity and diversity of tissue morphology, it is challenging to automatically detect and locate the cancer metastasis areas in panoramic images of the lymph nodes. In this paper, a method based on the deep cascaded network was proposed to realize the automatic localization and recognition of tumor metastasis region in panoramic images of breast lymph nodes. We implemented a coarse-to-fine model cascading method and a coarse positioning network VGG16 was first trained based on positive and negative image blocks extracted from doctor marked region, and then compared with the doctor marked region to extract the image blocks from the positive and false positive areas. The finely positioned ResNet50 network was trained to identify the positive and false-positive regions. The effectiveness of the deep cascaded network was verified with a Camelyon16 dataset, which included a total of 400 whole slide images for training and testing. The FROC value of the positioning index of the VGG16+ResNet50 cascaded network model proposed in this paper reached 0.891 2, which was 0.153 1 and 0.147 0 higher than the single deep network models VGG16 and ResNet50, and only 0.028 8 higher than AlexNet+VGG16 cascaded network model, showing that the deep cascaded network model could achieve more accurate identification of lymph node cancer metastasis regions.

Key words： panoramic pathological images detection of metastatic regions deep cascaded network

Received: 22 November 2019

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R318

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	Articles by authors
	Li Baoming
	Hu Jiarui
	Xu Haijun
	Wang Cong
	Jiang Yanni
	Zhang Zhihong
	Xu Jun

Cite this article:

Li Baoming,Hu Jiarui,Xu Haijun, et al. Deep Cascaded Network for Automated Detection of Cancer MetastasisRegion from Whole Slide Image of Breast Lymph Node[J]. Chinese Journal of Biomedical Engineering, 2020, 39(3): 257-264.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.03.01 OR http://cjbme.csbme.org/EN/Y2020/V39/I3/257

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