期刊
JOURNAL OF BIOPHOTONICS
卷 12, 期 7, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/jbio.201800435
关键词
classification; convolutional neural networks; differentiation grade; hepatocellular carcinoma (HCC); multiphoton microscopy (MPM)
资金
- National Key Basic Research Program of China [2015CB352006]
- National Natural Science Foundation of China [81771881]
- Natural Science Foundation of Fujian Province [2018J07004, 2018J01784, 2017J01743]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT_15R10]
- Science and Technology Planning Project of Guangdong Province [2016A020220014]
- Special Funds of the Central Government Guiding Local Science and Technology Development [2017L3009]
In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label-free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep-learning algorithm for classifying HCC differentiation to produce an innovative computer-aided diagnostic method. Convolutional neural networks based on the VGG-16 framework were trained using 217 combined two-photon excitation fluorescence and second-harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label-free, automated methods for various tissues, diseases and other related classification problems.
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