Cellular atypia automatic grading method based on deep learning and combination strategy

A deep learning and automatic grading technology, applied in character and pattern recognition, recognition of medical/anatomical patterns, instruments, etc., can solve the problems of high labor cost, time-consuming and laborious, etc., and achieve the effect of comprehensive grading results and less time consumption

Inactive Publication Date: 2016-11-09
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

In addition to being easily affected by subjective and environmental factors, manual analysis is also very time-consuming and labor-intensive, and the human cost is high

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  • Cellular atypia automatic grading method based on deep learning and combination strategy
  • Cellular atypia automatic grading method based on deep learning and combination strategy
  • Cellular atypia automatic grading method based on deep learning and combination strategy

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Embodiment Construction

[0025] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] A method for automatic grading of cell atypia based on deep learning and combination strategies of the present invention, comprising the following steps:

[0027] Step 1. Selection of training samples:

[0028] The training samples are constructed from the original data. The original data are marked by clinicians with professional pathological knowledge. The program will randomly select small square image blocks in the pathological images according to these expert marks. The side length of the block is 256 pixels. The program will build a training sample set corresponding to each resolution according to the number of image resolutions.

[0029] figure 1 is a schematic diagram of the degree of cellular atypia; figure 1 (a) is the histopath...

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Abstract

The invention discloses a cellular atypia automatic grading method based on deep learning and a combination strategy, comprising: first, under different resolutions, utilizing a deep learning method to identify the grade of a pathological tissue image; then, under each resolution, utilizing a trained depth model to process the large image under a current resolution combined with a sliding window method; utilizing one of combination strategies - an absolute majority vote method to determine the grade of the large image under a current resolution, thereby obtaining the grade of the large image under each current resolution; and finally utilizing a relative majority vote method to determine the final grade of the image out of grades of a plurality of resolutions. The cellular atypia automatic grading method employs big slice images as research objects, utilizes a deep learning and sliding window method and a combination strategy to accurately evaluate image cell atypia grades, helps doctors to estimate pathological tissue image cancer grades, and accurately and rapidly performs clinical diagnosis.

Description

technical field [0001] The invention relates to the technical field of image information processing, in particular to an automatic grading method for cell atypia based on deep learning and a combination strategy. Background technique [0002] With the generation of digital scanning technology for large slice images and the improvement of scanning efficiency, the digital display and storage of histopathological slides has become realistic and feasible. Higher quality analysis of pathological images is possible with digital technology. Because the characteristics of various cancer tissues can be found from the pathological imaging images of tissue slices, and can be used to assist doctors in diagnosis, but there are still few technical researches on medical image processing, so we study a set of pathological images. Analytical tools are very important. [0003] According to the diagnosis and treatment guidelines issued by the International Health Organization, the most widel...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06V2201/03G06F18/285G06F18/2193
Inventor 徐军周超
Owner NANJING UNIV OF INFORMATION SCI & TECH
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