Automatic interpretation system for cell pathology smear

Abstract

The invention discloses an automatic interpretation system for a cell pathology smear. The system comprises an imaging module; an image acquisition module; an image storage and management module; an image preprocessing module; the intelligent interpretation module receives the image output by the image preprocessing module, predicts and classifies a normal sample and a lesion sample for the image, and further predicts and interprets the lesion type when the image is the lesion sample; and the report writing module automatically generates an interpretation conclusion text of the sample corresponding to the image. According to the method, the artificial intelligence auxiliary diagnosis technology is successfully applied to the rapid staining cytopathology, the accuracy and consistency of diagnosis can be remarkably improved, and the workload of cytopathology doctors is reduced; according to the method, the existing convolutional neural network is improved, the multi-channel attention mechanism characteristics are utilized, uncertain factors introduced by manual sampling are solved, high-accuracy full-scene and multi-classification tasks can be achieved, and finally the accuracy of classification interpretation results can be improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence-assisted diagnosis and pathology, in particular to an automatic interpretation system for cytopathological smears. Background technique [0002] Lung cancer is a disease with the highest morbidity and mortality rate in the world. It also has an increasing trend year by year in my country, seriously endangering people's life and health. In 2017, the initial incidence of lung cancer in the world was 12.22 / 100,000, and the mortality rate was 19.88 / 100,000. According to data from the World Cancer Foundation, the incidence rate of lung cancer in China in 2018 was 35.1 / 100,000, ranking 16th in the world. According to data published by China's National Cancer Center in 2019, the incidence rate was 57.6 / 100,000. The mortality rate was 45.87 / 100,000. Since the disease has no special symptoms in the early stage, up to 2 / 3 of lung cancer patients are already in the middle-advanced stage when they seek m...

AI Technical Summary

Problems solved by technology

In 2017, teramoto (Teramoto A, Tsukamoto T, Kiriyama Y, et al. Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks [J]. Biomed Resint, 2017; 2017: 4067832.) etc. used deep convolutional neural networks ( DCNN) technology can distinguish adenocarcinoma, squamous cell carcinoma and small cell carcinoma in lung fine-needle aspiration liquid-based cell specimens, with an accuracy rate of 71.1%, and an accuracy rate of 85.6% in the distinction between non-small cell carcinoma and small cell carcinoma. The limitation is that it can only distinguish the main types of lung cancer, and cannot distinguish between benign and malignant of the specimen

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