基于深度学习的医学图像识别研究进展

doi:10.3969/j.issn.0258-8021. 2018. 01.012

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Abstract In recent years, with the rapid development of medical imaging technology, medical image analysis has entered the era of big data. How to extract useful information from a large number of medical image data has become one great challenge to medical image recognition. Deep learning is a new field of machine learning, conventional machine learning method can’t effectively extract enough information contained in the medical image, while the deep learning has the power of establishing a hierarchical model, powerful automatic feature extraction, complex model building and efficient feature expression through the simulation of the human brain. More importantly, deep learning method can extract the features from the bottom to the top level from the original data of the pixel level, which provides a new way to solve the new problems faced by medical image recognition. Based on a large number of domestic and foreign literatures, this paper elaborated the three methods of depth learning, enumerated three common implementation models of deep learning methods, and introduced the training process of depth learning. We summarized the application of deep learning in two aspects of disease detection and classification and lesions recognition, and summarized the two common problems in the application of deep learning in medical image recognition. The analysis and prospects of deep learning in medical image recognition problems were proposed and discussed as well.

Key words： medical image recognition machine learning deep learning

Received: 02 December 2016

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R318

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	Liu Fei
	Zhang Junran
	Yang Hao

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Liu Fei,Zhang Junran,Yang Hao. Research Progress of Medical Image Recognition Based on Deep Learning[J]. Chinese Journal of Biomedical Engineering, 2018, 37(1): 86-94.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2018. 01.012 OR http://cjbme.csbme.org/EN/Y2018/V37/I1/86

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