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Tumor detection for whole slide image of liver based on patch-based convolutional neural network

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Abstract

Liver cancer has a huge negative impact on the human survival. However, the traditional histopathological diagnostic methods have a large burden on the clinical diagnosis due to the large workload required. To this end, by combining the machine learning method and the whole slide images (WSIs), this paper proposes a novelty method to improve the efficiency of the clinical diagnosis. The core of our proposed method is to use a patch-based convolutional neural network to perform the category prediction (normal or tumor) on the patches extracted from the 60 liver tumor WSIs. Specifically, we design a total of four sets of the comparative experiment for the patch classification in order to screen the classifier with the best classification effect. The best classifier is then used to predict the category of patches in the testing set, and the results are combined to generate a probability heatmap to assist clinical diagnosis in an intuitive way. The experimental comparisons verify the validity of the proposed model which can achieve a classification accuracy rate of 99.94% in the patch classification task.

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Notes

  1. In the heatmap, the redder the area, the greater the probability that the area is a tumor area.

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Acknowledgements

This work is supported by the National Science Foundation of China (Nos.:61872311, 61972134, 61602156), Sorted by Key Science and Technology Program of Henan Province (No.:182102210053), Excellent Young Teachers Program of Henan Polytechnic University (No.:2019XQG-02).

Deidentified pathology images and annotations used in this research were prepared and provided by the Seoul National University Hospital by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI18C0316).

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Authors and Affiliations

  1. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China

    Jing Wang, Zhe Xu, Zhanqiang Huo & Junwei Luo

  2. School of Mathematics and Statistics, Henan University, Kaifeng, 475004, China

    Zhi-Feng Pang

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  1. Jing Wang
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Correspondence to Zhi-Feng Pang.

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Wang, J., Xu, Z., Pang, ZF. et al. Tumor detection for whole slide image of liver based on patch-based convolutional neural network. Multimed Tools Appl 80, 17429–17440 (2021). https://doi.org/10.1007/s11042-020-09282-x

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  • DOI: https://doi.org/10.1007/s11042-020-09282-x

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