Prostate cancer tissue microarray grading method based on convolutional neural network

Abstract

The invention provides a prostate cancer tissue microarray grading method based on a convolutional neural network. The method comprises the following steps: acquiring prostate cancer tissue microarrayimage data; preprocessing the prostate cancer tissue microarray image data; establishing an image segmentation model based on the preprocessed prostate cancer tissue microarray image data, and inputting the prostate cancer tissue microarray image data into the image segmentation model; restoring an output result of the image segmentation model in the size the same with that of the original image;and performing consistency check and comparison on the prediction result of the image segmentation model and the expert labeling result, and outputting a comparison result. According to the method, the characteristics of the deep layer and the shallow layer can be fused through the multi-scale self-attention network, meanwhile, the characteristics of each scale are supervised, network parameterscan be reduced, the calculation efficiency can be improved, and the effectiveness of the method is verified on a completely marked data set.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for grading a prostate cancer tissue microarray based on a convolutional neural network. Background technique [0002] In the latest global cancer statistics report, lung cancer is the most common among men (14.5%), followed by prostate cancer (13.5%), and the disease with the highest cancer incidence rate among men is prostate cancer has exceeded 100 countries. In the traditional diagnosis of prostate cancer, pathologists obtain case samples through needle biopsy, obtain pathological images after H&E staining, and observe the histomorphological pattern of cells under a microscope to confirm whether there is cancer in the tissue and perform Gleason grading. It is easily affected by subjective factors among pathology expert observers, and the manual annotation workload is time-consuming, time-consuming, and inefficient. [0003] The Gleason grading model is a wi...

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Problems solved by technology

[0005] The object of the present invention is to provide a method for grading prostate cancer tissue microarrays based on convolutional neural networks, to overcome or at least partially solve the above-mentioned problems existing in the existing Gleason automatic grading method based on classifiers

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