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Liver case image classification method and system based on deep neural network

A deep neural network and classification method technology, applied in the field of liver case image classification, can solve the problem of heavy labeling workload, achieve a good recognition effect, and reduce the labeling workload.

Pending Publication Date: 2020-09-15
TSINGHUA UNIV +1
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Problems solved by technology

[0002] In the case image recognition methods of the prior art, the classification and recognition are usually performed based on the tumor image with the marked position. In order to ensure the correctness of the classification, manual labeling is usually used, and the workload of labeling is large.

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  • Liver case image classification method and system based on deep neural network
  • Liver case image classification method and system based on deep neural network
  • Liver case image classification method and system based on deep neural network

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Embodiment Construction

[0044] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that these embodiments may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.

[0045] Various embodiments according to the present invention will be described in detail below with reference to the accompanying drawings.

[0046] figure 1 It is a flow chart of the liver case image classification method based on deep neural network according to the present invention, such as figure 1 Shown, described part case image classification method comprises:

[0047] Step S1, obtaining multiple case images belonging to the same user-set time period;

[0048] Step S2, resampling multiple case images to form a single 3D case image;

[0049] Step...

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Abstract

The invention provides a liver case image classification method and system based on a deep neural network. The method comprises the following steps: obtaining a plurality of case images belonging to the same time period set by a user; resampling the plurality of case images to form a single three-dimensional case image; inputting the single three-dimensional case image into a deep neural network model to extract image features, wherein the image features comprise an image color channel and position information; assigning weights to the image features by adopting an attention mechanism, whereinthe weights of the image features related to the liver are greater than the weights of other image features; and inputting the image features endowed with the weights into a classifier to obtain theclassification probability of a single three-dimensional case image, the classification which comprises lesion and normality. According to the method and the system, position information in the case image does not need to be marked.

Description

technical field [0001] The present invention relates to the technical field of image analysis, and more specifically, to a method and system for classifying images of liver cases based on a deep neural network. Background technique [0002] In the case image recognition methods in the prior art, the classification and recognition are usually performed based on the tumor images with marked positions. In order to ensure the correctness of the classification, manual marking is usually used, and the workload of marking is heavy. Contents of the invention [0003] The invention provides a liver case image classification method and system based on a deep neural network without labeling position information. [0004] According to one aspect of the present invention, a method for classifying images of liver cases based on a deep neural network is provided, including: [0005] Obtain multiple case images belonging to the same user-set time period; [0006] Resampling multiple cas...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2415G06F18/253G06F18/214
Inventor 江瑞章博亨陈传椿付俊丁宗仁李保晟
Owner TSINGHUA UNIV
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