Method for labeling and complementing gastric cancer pathological slice based on pseudo-label iterative annotation

Abstract

The invention discloses a method for labeling and complementing gastric cancer pathological slices based on pseudo-label iterative annotation. The method comprises the steps that 1), pseudo-label samples are produced by using the original positive samples and the original negative samples of the gastric cancer pathological slices; 2), image segmentation is conducted on the pseudo-label samples, and the pseudo-label samples are used as training images and transmitted to U-Net to be trained; 3), data augmentation is conducted on the original positive samples and transmitted to the trained U-Netin step 2) to be tested, reduction is conducted based on an augmentation manner, and finally weighted averaging is conducted on all images and the images are integrated to obtain a gastric diseased probability graph; 4), the parts of which the gastric cancer diseased probability is higher than a threshold value are screened out, extracted and spliced to the original negative samples to generate the pseudo-label samples of the next iteration; iteration is constantly conducted on the above processes to finally obtain the gastric cancer pathological slices which are completely annotated. By meansof the method, human resources needed to be consumed by slice annotation are greatly reduced, the quantity and quality of a training data set are improved, and probability is provided for training amore accurate deep learning model.

Description

technical field [0001] The invention belongs to the field of medical data mining, and in particular relates to a method for marking and completing gastric cancer pathological slices based on pseudo-label iterative labeling. Background technique [0002] Deep learning methods have made great achievements in the field of image processing, which also provides the possibility to apply deep learning technology to identify diseased parts in medical image data. At present, the CAD (computer-aideddiagnosis) system based on deep learning is widely used in identifying and segmenting organs and lesion areas in CT images. [0003] In 1998, LECUN and others first proposed the convolutional neural network (CNN) LeNet model, which was later used by many banks in the United States to recognize handwritten numbers on checks with high accuracy. Until 2012, the CNN model won the first place in the ImageNet competition. Since then, CNN has been widely used in the fields of image processing and...

AI Technical Summary

Problems solved by technology

[0007] The present invention aims to solve the problem of incomplete labeling of gastric cancer pathological slices in practical applications, and provides a method for completing labeling of gastric cancer pathological slices based on pseudo-label iterative labeling, which greatly reduces the human resources required for slice labeling and improves the efficiency of training data sets. Quantity and quality provide the possibility to train more accurate deep learning models

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