Abstract
There are multiple types of tumors occurring in the liver. Different tumors have different visual appearance and their visual appearance changes after injection of the contrast medium. So detection of liver tumors is considered as a challenging task. In this paper, we propose a method for detection of liver tumor candidates from CT images using a deep convolutional neural network. Experimental results show that we can significantly improve the detection accuracy by using our proposed method compared with the previous researches.
Keywords
- Deep learning
- Computer aided diagnosis (CAD)
- CT image
- Tumor candidate detection
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References
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Acknowledgments
This research was supported in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 15H01130, 15K00253 and No. 16H01436, in part by the MEXT Support Program for the Strategic Research Foundation at Private Universities (2013-2017), and in part by the Recruitment Program of Global Experts HAIOU Program from Zhejiang Province, China.
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Todoroki, Y., Han, XH., Iwamoto, Y., Lin, L., Hu, H., Chen, YW. (2018). Detection of Liver Tumor Candidates from CT Images Using Deep Convolutional Neural Networks. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2017. KES-InMed 2018 2017. Smart Innovation, Systems and Technologies, vol 71. Springer, Cham. https://doi.org/10.1007/978-3-319-59397-5_15
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DOI: https://doi.org/10.1007/978-3-319-59397-5_15
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Online ISBN: 978-3-319-59397-5
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