Cross-species medical image classification method based on domain self-adaption

A technology of medical imaging and classification methods, applied in the field of computer vision, can solve the problems of inability to extract effective features, and the different sizes of images of rats and humans.

Pending Publication Date: 2021-09-03
BEIJING UNIV OF TECH
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Problems solved by technology

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: first, the data of rats and humans are respectively input into the CNN-based multi-scale feature extraction network, thereby solving the problem that effective features cannot be extracted due to the different image sizes of rats and humans

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  • Cross-species medical image classification method based on domain self-adaption
  • Cross-species medical image classification method based on domain self-adaption
  • Cross-species medical image classification method based on domain self-adaption

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Embodiment Construction

[0019] The present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0020] The method model structure of the present invention is as figure 1 As shown, the flow chart of the method is as figure 2 shown, including the following steps:

[0021] Step 1. Expand the data, including clockwise rotation, random up-down flip, random left-right flip, diagonal transposition, and sub-diagonal transposition to increase the total amount of data. After random oversampling and gray level equalization The data of is stored in the form of a matrix, which is used as the input of the network model.

[0022] Step 2, using the multi-scale feature extraction module to extract the low-level features of the sample.

[0023] In step 2.1, the samples of the source domain and the target domain are input into the convolutional neural network SE-Net based on channel attention, and the low-level features of th...

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Abstract

The invention discloses a cross-species medical image classification method based on domain self-adaption, and the method comprises the steps of extracting domain invariant features and domain difference features between NAFLD image data of a rat and a human through a domain self-adaption algorithm; subjecting the uncalibrated human liver image data to category discrimination through a classifier trained by the calibrated rat liver image data by means of feature alignment and domain adversarial; building constraint based on distribution consistency of a source domain and a target domain and domain invariance constraint based on conditional adversarial learning, ensuring the mobility of a classifier by controlling the uncertainty of a classifier prediction result, and meanwhile, improving the discriminability of the classifier by calculating cross covariance between features and the classifier prediction result. Therefore, the pathological classifier suitable for the target field can be obtained on the medical image data with large sample distribution difference between the source field and the target field, and transfer learning of cross-species data is realized.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a domain-adaptive-based cross-species medical image classification method. Background technique [0002] Under the support and drive of national policies, the field of intelligence led by smart medical care has gradually attracted the attention of the society in recent years. Smart healthcare also utilizes advanced technology to realize the informatization interaction between patients and medical staff, medical institutions, and medical equipment. Among them, medical image intelligent analysis technology is an indispensable technology in intelligent medical treatment. It is widely used in medical image classification tasks, assists doctors in making decisions, further improves diagnostic efficiency, and reduces the pressure on patients to seek medical treatment. However, analysis techniques rely on large-scale standard calibration data, and these data are insufficient du...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06N3/04G06N3/08G16H30/20
CPCG06N3/08G16H30/20G06N3/045G06F18/24
Inventor 贾熹滨李启铭杨正汉曾檬杨大为任浩
Owner BEIJING UNIV OF TECH
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