Automatic pathological image labeling method based on reinforcement learning and deep neural network

Abstract

The invention discloses a pathological image automatic labeling method based on reinforcement learning and a deep neural network, and the method comprises the following steps: carrying out the multi-scale superpixel division of a dyed standardized original pathological image, and marking the epithelial probability threshold of superpixels; constructing a super-pixel classification training set; training the marked superpixels based on a machine learning method to obtain a learning model, classifying the superpixels in the test image by the learning model, awarding and punishing a classification result by a pathologist, feeding back awarding and punishing results to the learning model, and adjusting the learning model again through an awarding and punishing mechanism; constructing a training set of an end-to-end labeling model by adopting a pre-established method; adopting end-to-end learning based on a deep neural network to automatically mark and segment an organization region; constructing a labeling model; and testing the constructed annotation model by using pre-configured real data. The method has the beneficial effect that the learning models of different tissue regions can be quickly, accurately and automatically marked.

Description

technical field [0001] The present invention relates to the technical field of pathological image labeling, in particular to a method for automatic labeling of pathological images based on reinforcement learning and deep neural networks. Background technique [0002] There is a close relationship between different local areas of pathological images and the cause and severity of the disease. However, as the scale of pathological images continues to increase, the resolution of images continues to increase, and experienced pathologists are very scarce, and It is difficult for doctors to reach a consensus because of the subjectivity of film reading. This leads to problems such as long reading cycle and low accuracy in hospitals at this stage. Therefore, how to quickly and accurately mark various local areas of pathological images with the help of artificial intelligence, deep learning and other technologies, so as to select the most valuable diagnostic areas for the detection a...

AI Technical Summary

Problems solved by technology

However, manual labeling is time-consuming and labor-intensive, and subjectivity and fatigue lead to errors that cannot be ignored. With the continuous increase of clinical needs, the pressure on pathologists is also increasing

Images