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Semi-supervised cyclic self-learning method for few lumbar vertebra medical image samples and model

A self-learning method and medical imaging technology, applied in the field of semi-supervised loop self-learning methods and models, can solve the problems of high medical image labeling costs and reduce medical image labeling costs, achieve a stable training process, reduce the risk of underfitting, The effect of improving robustness

Active Publication Date: 2021-04-06
杭州健培科技有限公司
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Problems solved by technology

[0006] The purpose of the present invention is to provide a semi-supervised loop self-learning method and model for few-sample lumbar spine medical images. The learning strategy is designed to obtain a large-capacity variable-capacity positioning model, which can be used to accurately locate the key points of medical images and reduce the cost of labeling medical images

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  • Semi-supervised cyclic self-learning method for few lumbar vertebra medical image samples and model
  • Semi-supervised cyclic self-learning method for few lumbar vertebra medical image samples and model
  • Semi-supervised cyclic self-learning method for few lumbar vertebra medical image samples and model

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Embodiment Construction

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention belong to the protection scope of the present invention.

[0023] This program provides a semi-supervised cyclic self-learning method and model for few-sample lumbar medical images. Computer software or programs that can run the semi-supervised cyclic self-learning method for few-sample lumbar medical images are loaded in the device. The following is for the convenience of explanation , using the semi-supervised loop self-learning method as an abbreviation for the description of the scheme.

[0024] The semi-supervised loop self-learning method of th...

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Abstract

The invention provides a semi-supervised cyclic self-learning method for few lumbar vertebra medical image samples and a model and aims to solve the problem of high medical image labeling cost. Based on a few labeled samples and a large number of unlabeled samples, a high-capacity capacity variable positioning model is designed by using a cyclic self-learning strategy. The method can be used for accurately positioning the key points of a medical image and reducing the labeling cost of the medical image.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a semi-supervised loop self-learning method and model for few-sample lumbar spine medical images. Background technique [0002] Medical imaging technology has experienced more than 100 years of development, among which MRI, CT, DR, ultrasound and other technologies have been widely used in various clinical medical diagnoses. The development of medical imaging technology has greatly improved the efficiency and accuracy of diagnosis, providing guidance for subsequent treatment and surgery. [0003] At present, a large number of residents in my country are troubled by lumbar-related diseases. The positioning measurement of the lumbar spine in medical imaging of the lumbar spine can provide a reference for the diagnosis of various diseases, such as the rotation angle of the lumbar spine, the Cobb angle of lumbar scoliosis, the transverse diameter of the lumbar spinal canal, and the il...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06N3/08G06T2207/10124G06T2207/20101G06T2207/20081G06T2207/20084G06T2207/30012G06N3/045G06F18/40G06F18/241
Inventor 罗梦研程国华何林阳季红丽周晟陈晓飞
Owner 杭州健培科技有限公司
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