Cervical cancer tct slice vaginal discharge method and system based on deep learning

A technology of deep learning and cervical cancer, applied in the field of medical image analysis, can solve the problems of inability to interpret TCT images of cervical cancer, cell size, sensitivity to fine-grained features, errors, etc., to avoid recognition defects, good robustness, and ensure stability Effects on Sex and Reliability

Active Publication Date: 2021-11-23
杭州医策科技有限公司
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Problems solved by technology

However, the target detection model is particularly sensitive to the size and fine-grained characteristics of cells. In some atypical lesion samples, there may be false positive cells among the positive cells in the detection results, and there are certain errors. Direct use of the target detection model often cannot accurately and effectively Overall Interpretation of TCT Images of Cervical Cancer

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  • Cervical cancer tct slice vaginal discharge method and system based on deep learning
  • Cervical cancer tct slice vaginal discharge method and system based on deep learning
  • Cervical cancer tct slice vaginal discharge method and system based on deep learning

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[0048] In order to further understand the present invention, the preferred embodiments of the present invention are described below in conjunction with examples, but it should be understood that these descriptions are only to further illustrate the features and advantages of the present invention, rather than limiting the claims of the present invention.

[0049] Application overview

[0050] The length and width scales of pathological images generally range from thousands to hundreds of thousands of pixel units, and the cells included can reach more than 100,000 levels. Pathologists need to carry out detailed diagnosis of cells in positive slide images. In fact, About 90% of the slides are negative for cervical cancer, which consumes a lot of time for doctors. How to use deep learning methods to assist doctors to effectively exclude negative slides is a technical problem that needs to be solved urgently. A deep learning target detection model is trained by a large number of T...

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Abstract

The present invention provides a cervical cancer TCT slice vaginal discharge method based on deep learning. The detection model detects each image block and outputs the cervical cancer positive target detection frame and the corresponding detection frame confidence under the image block; sets the detection frame confidence threshold, and filters and retains the detection frame confidence based on the detection frame confidence threshold The detection frame whose detection degree is greater than the confidence threshold of the detection frame; use the negative classification model to detect the detected target image in the retained detection frame, output the positive probability of all detected target images, and calculate the overall average positive probability; set Negative discharge probability threshold, if the overall average positive probability is less than or equal to the preset negative discharge probability threshold, it is determined that the target image is negative as a whole, otherwise it is determined that the image is to be manually checked, and the doctor will manually check it.

Description

technical field [0001] The present invention relates to the field of medical image analysis, in particular to a method and system for cervical cancer TCT slice vaginal discharge based on deep learning. Background technique [0002] At present, cervical cancer is one of the most common malignant tumors in women. There are about 130,000 new cases in China every year. The incidence rate is second only to breast cancer among female malignant tumors. Thinprep Cytologic Test (TCT) can achieve the purpose of early diagnosis and early treatment of patients. Doctors need to conduct cytological examination on TCT slides of each patient, which is the most important cervical cancer screening at present. means. According to statistics, the population of cervical cancer screening in my country is as high as 350 million, but there are currently less than 20,000 registered pathologists in China, and it takes 5 to 10 years to train an experienced pathologist. The huge shortage of doctors le...

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G06T7/00G06T7/11G06K9/62
CPCG06T7/0012G06T7/11G06T2207/20076G06T2207/20081G06T2207/30096G06F18/24
Inventor王晓梅范晓华蔡博君张仕侨章万韩朱逢亮
Owner杭州医策科技有限公司