Pathological section color normalization method and system

Abstract

The invention discloses a pathological section color normalization method and system, and the method comprises the steps: generating a picture of a target style through a generator, carrying out the discrimination of the generated picture and a real picture of the target style through a discrimination network, and executing the learning and discrimination adversarial training in a domain. In orderto reduce the difference between the generated picture of the non-target style picture and the target style picture, the generated picture of the non-target style picture and the target style pictureare identified through another identification network, inter-domain learning and identification confrontation training are executed, the difference between the generated picture of the non-target style picture and the target style picture is further reduced, and the performance of the generation network is optimized. According to the method, color normalization is carried out on pathological section data of different color styles, and the technical problems that a depth model trained under a single color style is difficult to have the same or similar performance in data of another color style, and the model is difficult to converge when pathological sections of different color styles are used as data to train the depth model are solved.

Description

technical field [0001] The invention belongs to the field of medical cytopathological image analysis, and more specifically relates to a method and system for normalizing the color of cytopathological slices from different sources. Background technique [0002] In recent years, artificial intelligence technology has developed rapidly, and the combination of artificial intelligence and medical care can alleviate the problem of shortage of doctor resources. In the field of medical cytopathology, the accumulation of a large amount of pathological slice data provides a big data background for the analysis of medical cytopathological images. In the processing of large data samples, because the analysis and processing capabilities of deep learning algorithms are generally stronger than other traditional analysis algorithms, depth Learning is widely used in the field of big data medical cytopathology image analysis. [0003] Using deep learning to analyze medical cytopathological ...

AI Technical Summary

Problems solved by technology

[0006]Aiming at the defects or urgent technical needs of the prior art, the present invention proposes a method and system for normalizing the color of pathological slices. Color-style pathological slice data is color-normalized to solve the problem that it is difficult for a depth model trained in a single color style to have the same or similar performance in another color-style data. When different color-style pathological slices are used as data to train a depth model, the model will Difficult to converge technical issues

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