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Model training method and device, image recognition method and device

A technology for model training and image recognition, applied in the field of neural networks, can solve problems such as low accuracy, and achieve the effect of improving recognition accuracy

Active Publication Date: 2021-12-28
浙江太美医疗科技股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] However, the accuracy of existing part recognition methods and part extraction methods is low

Method used

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  • Model training method and device, image recognition method and device
  • Model training method and device, image recognition method and device
  • Model training method and device, image recognition method and device

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Embodiment Construction

[0032] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0033] As is well known, in general, a three-dimensional medical image sequence includes multiple parts. For example, the 3D medical image sequence is the medical image sequence of the entire human body, including 13 parts, namely the brain, cerebral nasopharynx, nasopharynx, nasopharynx, neck, neck, cervicothorax, chest, thoracic abdomen, abdomen, abdomen and pelvis, Pelvic, pelvic lower extremities and lower extremities. ...

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Abstract

The application provides a model training method and device, and an image recognition method and device, which relate to the technical field of neural networks. The model training method includes: using the first recognition model to determine the first category recognition results corresponding to the M images based on the first category distribution weight data corresponding to the M images respectively; based on the category label data and the first category label data corresponding to the M images A category recognition result, determine the category consistency rate data corresponding to each of the M images; determine the second category distribution weight data corresponding to each of the M images based on the category consistency rate data corresponding to the M images; The second category assigns weight data to train the first recognition model to obtain an image recognition model. The present application can redefine the learning difficulty weights for wrongly predicted image samples, thereby making the model pay more attention to wrongly predicted image samples, and greatly improving the recognition accuracy of the trained image recognition model.

Description

technical field [0001] The present application relates to the technical field of neural networks, and in particular, to a model training method and device, and an image recognition method and device. Background technique [0002] Typically, a three-dimensional medical image sequence includes multiple parts. In order to facilitate the operation of the reader, it is necessary to determine the part to which each image in the 3D medical image sequence belongs (ie, determine the corresponding category of the image in the 3D medical image sequence), so as to extract the image corresponding to the part based on the part. [0003] However, the existing part recognition methods and part extraction methods have low accuracy. SUMMARY OF THE INVENTION [0004] In order to solve the above technical problems, the present application is made. Embodiments of the present application provide a model training method and device, and an image recognition method and device. [0005] In a fir...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24G06F18/214
Inventor 蔡鑫崔亚轩邱慎杰
Owner 浙江太美医疗科技股份有限公司