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Pulmonary nodule benignity and malignancy predicting method based on convolutional neural networks

A technology of convolutional neural network and prediction method, which is applied in the field of prediction of benign and malignant pulmonary nodules based on convolutional neural network, can solve inaccurate problems and achieve accurate prediction effect

Inactive Publication Date: 2015-06-10
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

Different initializations will have different effects on the final segmentation results, therefore, the features obtained by using such segmentation results are usually inaccurate

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  • Pulmonary nodule benignity and malignancy predicting method based on convolutional neural networks
  • Pulmonary nodule benignity and malignancy predicting method based on convolutional neural networks
  • Pulmonary nodule benignity and malignancy predicting method based on convolutional neural networks

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Embodiment Construction

[0011] The general idea of ​​the present invention is to construct the respective convolutional neural networks corresponding to multiple pulmonary nodule image blocks according to multiple pulmonary nodule image blocks, and obtain the low-dimensional features corresponding to the multi-dimensional image blocks according to each convolutional neural network, using Low-dimensional features train a nonlinear classifier to accurately predict unknown pulmonary nodule image patches.

[0012] The method for predicting benign and malignant pulmonary nodules based on a convolutional neural network provided by an embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

[0013] figure 1 It is a flowchart of a method for predicting benign and malignant pulmonary nodules based on a convolutional neural network provided by an embodiment of the present invention.

[0014] refer to figure 1 , in step S101, a multi-scale pulmonary n...

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Abstract

The invention provides a pulmonary nodule benignity and malignancy predicting method based on convolutional neural networks. The pulmonary nodule benignity and malignancy predicting method based on the convolutional neural networks comprises obtaining multi-scale pulmonary nodule image blocks from pulmonary nodule coordinates; according to the multi-scale pulmonary nodule image blocks, structuring the convolutional neural network corresponding to every multi-scale pulmonary nodule image block; according to a loss function, training the convolutional neural networks through the multi-scale pulmonary nodule image blocks; extracting low-dimensional features of pulmonary nodules through the trained convolutional neural networks; training a non-linear classifier through the low-dimensional features and predicting unknown pulmonary nodule image blocks. The pulmonary nodule benignity and malignancy predicting method based on the convolutional neural networks can accurately predict the benignity and malignancy of the unknown pulmonary nodule image blocks.

Description

technical field [0001] The invention relates to the field of medical images, in particular to a method for predicting benign and malignant pulmonary nodules based on a convolutional neural network. Background technique [0002] The current benign and malignant pulmonary nodule extraction methods include: pulmonary nodule segmentation; feature extraction; classifier training, the above methods rely on the pre-segmentation of pulmonary nodules. Many current segmentation methods rely on the initialization of algorithms, such as region growing algorithms, level set algorithms, etc. Different initializations will have different effects on the final segmentation results, therefore, the features obtained by using such segmentation results are usually inaccurate. Contents of the invention [0003] The method for predicting benign and malignant pulmonary nodules based on the convolutional neural network provided by the present invention can accurately predict unknown pulmonary nod...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 田捷沈伟杨凤杨彩云
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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