Microscopic image quality analysis method and system, training method and system, equipment and medium
A technology for microscopic image and quality analysis, applied in the field of medical pathology image processing and image processing, can solve the problem of difficult to effectively analyze and judge the quality of microscopic images, and achieve the effect of improving the intelligence and accuracy of diagnosis and improving the quality.
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0055] Such as figure 1 As shown, this embodiment provides a microscopic image quality analysis method, and the microscopic image quality analysis method includes the following steps:
[0056] Step S100, taking partial images of each region in the pathological microscopic image sequentially according to the shooting direction;
[0057] Step S200, using one or more pre-trained image quality analysis models to perform quality analysis on at least one of the partial images input each time;
[0058] Step S300, when the preset quality abnormality output condition is met, output and display the abnormal quality analysis result, and control to stop shooting partial images in the pathological microscopic image.
[0059] Steps S100 to S300 of the microscopic image quality analysis method of this embodiment will be described in detail below.
[0060] Step S100, sequentially capture partial images of each region in the pathological microscopic image according to the shooting direction....
Embodiment 2
[0092] Such as figure 2 As shown, in this embodiment, a method for training and generating an image quality analysis model for microscopic image quality analysis, the method for training and generating an image quality analysis model includes:
[0093] Step S211, obtaining an image classification training set with classification labels; wherein, the image classification training set includes several partial images without abnormal quality, several partial images with first-type abnormal quality labels, and several second-type abnormal quality labels. Partial images...several partial images of Nth class abnormal quality labels;
[0094] Step S212, input the image classification training set training into the convolutional neural network model for iterative training, obtain the input partial image that can be classified, and output the classification results as no quality abnormality, first-class quality abnormality, The second type of quality anomaly...an image quality analys...
Embodiment 3
[0109] Such as image 3 As shown, this embodiment provides a method for training and generating an image quality analysis model for microscopic image quality analysis, and the method for training and generating an image quality analysis model includes:
[0110] Step S311, obtaining an image classification training set with a classification label; wherein, the image classification training set includes several partial images without object type abnormal quality, and several partial images with object type abnormal quality labels;
[0111] Step S312, input the image classification training set into the convolutional neural network model for iterative training, and obtain an image quality analysis model that can classify the input partial images and obtain a classification result of whether there is an abnormal quality of the target type;
[0112] The abnormal quality of the target type is any one of out-of-focus, cell stacking, too light staining, too deep staining, air bubbles,...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More