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Deep learning based classification for metastasis of hepatocellular carcinoma with microscopic images
Author(s): Hui Meng; Yuan Gao; Kun Wang; Jie Tian
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Paper Abstract

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide. The high probability of metastasis makes its prognosis very poor even after potentially curative treatment. Detecting high metastatic HCC will allow for the development of effective approaches to reduce HCC mortality. The mechanism of HCC metastasis has been studied using gene profiling analysis, which indicated that HCC with different metastatic capability was differentiable. However, it is time consuming and complex to analyze gene expression level with conventional method. To distinguish HCC with different metastatic capabilities, we proposed a deep learning based method with microscopic images in animal models. In this study, we adopted convolutional neural networks (CNN) to learn the deep features of microscopic images for classifying each image into low metastatic HCC or high metastatic HCC. We evaluated our proposed classification method on the dataset containing 1920 white-light microscopic images of frozen sections from three tumor-bearing mice injected with HCC-LM3 (high metastasis) tumor cells and another three tumor-bearing mice injected with SMMC-7721(low metastasis) tumor cells. Experimental results show that our method achieved an average accuracy of 0.85. The preliminary study demonstrated that our deep learning method has the potential to be applied to microscopic images for metastasis of HCC classification in animal models.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492L (15 March 2019); doi: 10.1117/12.2512214
Show Author Affiliations
Hui Meng, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Yuan Gao, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Kun Wang, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Jie Tian, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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