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Intelligent prediction system for pathological indexes

A technology of intelligent prediction and index, applied in the field of intelligent prediction system of pathological index, to achieve the effect of improving performance, eliminating differences, and improving effectiveness

Active Publication Date: 2020-12-11
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0004] In summary, the existing pathological slice MSI prediction methods need to be further improved in terms of improving the effectiveness of image block-level prediction data, model network structure selection, and overall prediction framework design.

Method used

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  • Intelligent prediction system for pathological indexes
  • Intelligent prediction system for pathological indexes
  • Intelligent prediction system for pathological indexes

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Embodiment Construction

[0042] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0043] Such as figure 1 As shown, an intelligent prediction system for pathological indicators includes a data set acquisition module, a color specification module, a specific structure filter, a pathological indicator predictor, and a weighting module.

[0044] The data set acquisition module is used to classify the pathological slice images according to the set pathological indicators, and then divide the pathological slice images of each category into image blocks of a set size to obtain the original data set of the image blocks, wherein the The number of pathological index categories contained in the image block original data set is recorded as K categories.

[004...

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Abstract

The invention provides an intelligent prediction system for pathological indexes. Firstly, the shape categories of patterns in image blocks are classified, and then, the image blocks are input to pathological index predictors corresponding to different shape categories to obtain a classification result of each pathological index predictor. and finally, according to the number of the image blocks contained in different combinations formed by thee pathological index categories and the shape categories and the weight of each pathological index predictor, the score of a certain pathological section image belonging to each pathological index category is calculated, the pathological index category corresponding to the maximum score is taken as a final pathological index prediction result of a pathological section image. Therefore, the image blocks are classified more meticulously, and validity analysis is performed on the image blocks of various shapes and categories, so that the validity ofimage block-level prediction data and the predictors and the accuracy of a patient-level prediction result can be obviously improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent medical equipment, and in particular relates to an intelligent prediction system for pathological indicators. Background technique [0002] Medical imaging pathological index prediction technology is one of the key technologies in the field of intelligent medical care. At present, in the medical field, deep learning technology can be used to analyze medical images and improve the accuracy of prediction results. The pathological slice microsatellite instability (MSI) prediction method proposed by the German National Cancer Center in 2019 is one of the representative technologies. This technology divides the overall pathological slice image into smaller image blocks, uses the deep learning model to perform MSI prediction on the segmented image blocks, obtains the block-level prediction results, and finally obtains the patient-level prediction by voting As a result, the accuracy of prediction r...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/70G06K9/62G06T7/00G06T7/90
CPCG16H50/70G06T7/0012G06T7/90G06T2207/20081G06T2207/20084G06T2207/30096G06V2201/03G06F18/241G06F18/214
Inventor 马旭蔡家铭赵琦乐
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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