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Artificial intelligence deep learning method and medical radiographic image chest radiography quality control method based on artificial intelligence

A quality control method and artificial intelligence technology, applied in medical imaging, medical informatics, informatics, etc., can solve problems such as low efficiency of manual quality control and inconsistent standards

Active Publication Date: 2019-09-20
辽宁万象联合医疗科技有限公司
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AI Technical Summary

Problems solved by technology

[0005] In view of this, the present invention discloses an artificial intelligence-based deep learning method and an artificial intelligence-based quality control method for medical radiographic chest radiography, so as to at least solve the problems of low efficiency and non-uniform standards in manual quality control.

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  • Artificial intelligence deep learning method and medical radiographic image chest radiography quality control method based on artificial intelligence
  • Artificial intelligence deep learning method and medical radiographic image chest radiography quality control method based on artificial intelligence
  • Artificial intelligence deep learning method and medical radiographic image chest radiography quality control method based on artificial intelligence

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Embodiment Construction

[0052] The present invention will be further explained with specific examples below, but it is not intended to limit the protection scope of the present invention.

[0053]In view of the problems of low efficiency and non-uniform standards in manual quality control in the prior art, this embodiment provides a quality control method for medical radiographic chest radiographs based on artificial intelligence. The quality control method includes two parts: 1) building and training the artificial intelligence model; 2) two parts of quality control identification.

[0054] Build and train an AI model:

[0055] Among them, see figure 1 The artificial intelligence model constructed for the present invention, the artificial intelligence model includes: the first convolutional layer 1, the first pooling layer 2, the second convolutional layer 3, the second pooling layer 4, the third convolutional layer 5, The third pooling layer 6, the fourth convolutional layer 7, the first upsampli...

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Abstract

The invention discloses an artificial intelligence deep learning method and a medical radiographic image chest radiography quality control method based on artificial intelligence. Based on the previous continuous convolution pooling operation, the quality control method adds a step of performing spliced convolution on a result of a last convolution pooling operation and a result of a previous convolution pooling operation, and carrying out secondary splicing convolution on the convolution pooling operation result of the previous level; and performing splicing convolution on a result of the first convolution pooling operation. Through optimization of the learning method, the method not only has the advantage of previous continuous convolution pooling, but also can process missing data in the previous continuous convolution pooling process again through step-by-step splicing convolution, and finally achieves the purpose of deep learning.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence deep learning method and an artificial intelligence-based quality control method for medical radiographic chest radiographs. Background technique [0002] The chest X-ray is the χ-radiograph of the chest. When the chest X-ray is taken, the subject takes a standing position, and generally holds the breath under a calm inhalation to project. Cardiovascular conventional chest radiographs include: posteroanterior view (focal-film distance 200cm), left anterior oblique view (60°-65°), right anterior oblique view (45°-55°) and left lateral view. Frontal chest radiography can show the size, shape, position and outline of the great vessels of the heart, and can observe the relationship between the heart and adjacent organs and the changes of blood vessels in the lungs, and can be used to measure the heart and its diameter. The left ante...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G16H30/00G16H50/20
CPCG06T7/0012G16H50/20G16H30/00G06T2207/30168G06N3/045
Inventor 刘景鑫王静石莫展豪张宇田中生张忠祖莅惠史申刘婉华肖长斌
Owner 辽宁万象联合医疗科技有限公司
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