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Method for focus classification of sectional medical images by employing deep learning network

A deep learning network and medical imaging technology, applied in neural learning methods, biological neural network models, understanding of medical/anatomical patterns, etc., can solve the problem of inability to differentiate medical imaging lesions, and achieve the effect of improving work efficiency

Active Publication Date: 2016-06-29
WUHAN KEARNS MEDICAL SCI & TECH CO LTD
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a method for classifying lesions on tomographic medical images using a deep learning network in view of the defects in the prior art that cannot effectively distinguish lesions from tomographic medical images

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  • Method for focus classification of sectional medical images by employing deep learning network
  • Method for focus classification of sectional medical images by employing deep learning network
  • Method for focus classification of sectional medical images by employing deep learning network

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Embodiment Construction

[0037] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0038] In the embodiment of the present invention, the method for classifying lesions on tomographic medical images using a deep learning network, taking the tomographic medical images as MRI tomographic images as an example, mainly includes the following steps:

[0039] S1 case data labeling

[0040] Let case be X i , i∈[1,...,N], where N is the total number of cases, and each case has a label Y i , indicating whether it is PCA or BPH, say, if X i is PCA, then Y i = 1; if x i is BPH, then Y i = 2; in the case of MRI irradiation, each case has multi-layer image data, so the case can be repres...

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Abstract

The invention discloses a method for focus classification of sectional medical images by employing a deep learning network. The method includes following steps: S1, collecting data of case sectional medical images, and marking the case sectional medical images to form a training set ; S2, pre-processing the sectional medical images; 3, extending the data in the training set; S4, building and training a DCNN classification model; and S5, inputting the data and processing a prediction result. According to the method, radiologists can be helped to rapidly distinguish the kind of focuses so that the work efficiency of doctors can be greatly improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for classifying lesions on tomographic medical images using a deep learning network. Background technique [0002] There are many kinds of tomographic medical images, such as MRI (Magnetic Resonance Imaging), which is a type of tomographic imaging. It uses magnetic resonance phenomena to obtain electromagnetic signals from the human body and reconstruct human body information. [0003] Deep learning is a very cutting-edge machine learning method. Its motivation is to establish and simulate the neural network of the human brain for analysis and learning. It imitates the mechanism of the human brain to interpret data, such as images, sounds and texts. The concept of deep learning originated from the study of artificial neural networks. A multi-layer perceptron with multiple hidden layers is a deep learning structure. Deep learning combines low-level features to form more ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06V2201/03G06F18/2415
Inventor 王兴刚罗博王良欧阳杰刘涛杨巍贺松平
Owner WUHAN KEARNS MEDICAL SCI & TECH CO LTD
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