Rapid diagnosis and scoring method for full-scale pathological section based on deep learning

Abstract

The invention relates to a rapid diagnosis and scoring method for a full-scale pathological section based on deep learning. Preprocessing is carried out on a full-scale pathological section staining map; the node number of a full-connection layer and an output layer of a traditional AlexNet neural network is changed to meet the needs of practical problems, a marked training sample set is selectedto train two AlexNet neural network models for diagnosis and scoring, and high-dimensional feature information of a lesion area is extracted; with the two improved AlexNet neural network models aftertraining, the full-scale pathological section staining map is diagnosed and scored; and according to a diagnosis predicted probability, a probability heat map is drawn and the lesion area is identified visually, statistics of proportions of small sampling block numbers with different lesion degrees is carried out, and the lesion degree of a tissue is scored. Therefore, the diagnosis and Gleason scoring of the full-scale pprostate tissue pathological section are realized automatically; and the accuracy rate and the calculation rate exceed the average level of the artificial diagnosis substantially.

Description

technical field [0001] The invention relates to the fields of image processing and medicine, in particular to a method for rapid diagnosis and scoring of full-scale pathological slices based on deep learning. Background technique [0002] In my country, malignant tumors have already become the primary cause of death for urban and rural residents, and the mortality rate of malignant tumors is at a high level in the world, and it shows a continuous growth trend. Prostate cancer is the most common malignant tumor of the male reproductive system, and its morbidity and mortality are second only to lung cancer, ranking second in cancer deaths. Currently, H&E stained pathological sections are still the gold standard for diagnosing tissue cancer. However, pathological diagnosis often relies on the subjective judgment of pathologists, which is likely to cause misdiagnosis and missed diagnosis. At the same time, due to the uneven professional level of pathologists and the uneven geo...

AI Technical Summary

Problems solved by technology

[0003] 1. At present, most of the pathological diagnosis of prostate tissue is just a binary classification, that is, judging whether it is normal or cancer tissue, and rarely scoring the severity of prostate cancer;

[0004] 2. Traditional cancer detection mainly uses local detection of cell nuclei, and seldom focuses on staining images of full-scale pathological sections;

[0005] 3. Using the method of texture analysis plus classifier, features need to be manually extracted, and the accuracy rate is only about 85%;

[0006] 4. The calculation efficiency is low, and the diagnosis takes a long time

Images