Non-small cell lung cancer subtype classification system based on multi-view deep learning

Abstract

The invention discloses a non-small cell lung cancer subtype classification system based on multi-view deep learning, which realizes classification of lung adenocarcinoma and lung squamous carcinoma based on a CT image, generates and displays image data of an examined part based on volume data generated by performing lung CT on an examined body, and obtains non-small cell lung cancer subtype classification result of the examined body. The non-small cell lung cancer pathological subtype classification system comprises an information acquisition module, a network module and a training module. According to the system, automatic lung field segmentation can be realized, interlayer information and three-dimensional information carried by a CT image are fully utilized by a multi-view model, an obtained classification model can be used as a tool for assisting a doctor through automatic feature extraction and classification training, and the system has the characteristics of high automation and high practicability.

Description

technical field [0001] The invention belongs to the field of artificial intelligence deep learning and biological information technology, and in particular relates to a non-small cell lung cancer subtype classification system based on multi-view deep learning. Background technique [0002] Worldwide, the incidence of lung cancer accounts for 11.6% of new tumors, and its mortality rate accounts for 18.4% of all malignant tumor deaths. a great threat. Among lung cancers, non-small cell lung cancer accounts for 85-90%. [0003] According to the World Health Organization's 2015 standards, non-small cell lung cancer can be further divided into adenocarcinoma (ADC), squamous cell carcinoma (Squamous Cell Carcinoma, SCC), large cell carcinoma (LargeCell Carcinoma, LCC), and unspecified (Not Otherwise Specified, NOS). Among them, the incidence of lung adenocarcinoma is increasing year by year, which is the most common subtype of NSCLC, accounting for almost 60% of NSCLC, and the ...

AI Technical Summary

Problems solved by technology

[0004] Pathological diagnosis is currently the gold standard for subtype classification of non-small cell lung cancer clinically, however, it requires invasive biopsy or pathological tissue section, which often brings severe pain to patients

In the actual sampling operation, due to the spatiotemporal heterogeneity of lung cancer, the sampling results are also difficult to describe the whole picture of the tumor, so the accuracy is often questioned

In addition, since the pathological tissue is prepared postoperatively, the diagnosis requires a series of molecular biological steps, so the treatment of the patient may be delayed due to these time costs

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