Pathological image automatic identification system based on deep learning and training method thereof

Abstract

The invention provides a pathological image automatic identification system training method based on deep learning, which is characterized by comprising the following steps: step (a), performing imagesegmentation; (b) acquiring a tumor region of interest and a lymph node region of interest; (c) extracting imaging omics characteristics in the tumor region of interest and the lymph node region of interest; (d) eliminating redundant image omics characteristics; (e) determining optimal imaging omics characteristics in the tumor region of interest and the lymph node region of interest; (f) constructing a training set by using the optimal image omics characteristics, and predicting lymph node metastasis; (g) constructing an independent verification set to verify the training set; (h) when the prediction effectiveness of the training set reaches a preset value, ending the training; and when the prediction effectiveness of the training set is lower than a preset value, reconstructing the training set for training. The training method can improve the effectiveness of predicting gastric cancer lymph node metastasis.

Description

technical field [0001] The present invention relates to the field of image recognition, in particular to a deep learning-based training method for an automatic pathological image recognition system, and an automatic pathological image recognition system trained by the training method. Background technique [0002] Gastric cancer is one of the most common malignant tumors in the world, with a poor prognosis and a serious threat to human health. According to the latest statistics from GLOBOCAN, in 2018, there were about 1.033 million new cases of gastric cancer worldwide, and about 783,000 deaths, ranking fifth in terms of incidence and second in mortality among malignant tumors. According to the latest cancer statistics in my country, there are about 679,000 new cases of gastric cancer each year, and about 498,000 deaths. The number of cases and deaths ranks second among all malignant tumors. In gastric cancer, lymph node metastasis is considered to be an important prognosti...

AI Technical Summary

Problems solved by technology

[0003] Full-scan slicing technology can realize the digitization of pathological slices. With the development of full-slice image acquisition, processing and analysis technology, digital pathology has been gradually applied to scientific research and clinical practice in the past ten years. However, at present, in daily pathological slices During the diagnosis process, doctors usually detect and classify the metastatic lymph nodes in the whole slice by reading one by one, and screen out the image and location of the metastatic lymph nodes. Therefore, the workload of doctors is very heavy and the efficiency is low

Images