Detection Of Microvasular Invasion Hepatocelluar Carcinoma Based On Mutilmodal Deep Learning

Hepatocellular carcinoma is one of the highest morbidity and mortality among all diseases,which seriously threatens the lives of modern people.According to data released by the National Cancer Center in 2018,the incidence and mortality of liver cancer in men are ranked in the top five.The treatment of hepatocellular carcinoma is mainly surgical removal of the affected tumor area.The risk of recurrence after hepatocellular carcinoma surgery is very high.According to the survey,the probability of recurrence within five years after hepatocellular carcinoma surgery is as high as 60 % ?70 %.Vascular invasion is of great significance for the recovery of hepatocellular carcinoma after surgical resection[1].Hepatic cell carcinoma radical resection is performed to detect whether vascular invasion has occurred in patients with hepatocellular carcinoma,and it also has important guidance for subsequent surgical operations significance.Breakthroughs have been made in such fields as text.In terms of medical treatment,deep learning has been applied in medical aspects such as lung nodule detection.This article will use deep learning technology for the first time to automatically detect vascular invasion of hepatocellular carcinoma.In this paper,a multimodal vascular invasion detection model based on convolutional neural network is studied for abdominal CT tomography data images and biochemical data.It is used to assist doctors in diagnosis in order to provide guidance for hepatocellular carcinoma resection.The model mainly consists of four parts.The first part is image preprocessing,and the second part is image feature extraction module,the third part is biochemical data extraction module,the last part is integrated voting stage.In the image preprocessing stage,the mask layer is mainly cut,so that the input data is more focused on the location of the lesion under study.Finally,the data is standardized to better meet the input requirements of the network.The image feature module mainly uses a convolutional network to extract lesion features.In order to solve the problem of low data volume,this paper cuts the 3D stereoscopic scan images in the tomographic direction,and separately trains the slices of tumors at similar positions,and trains eight independent convolutional network models at the image feature stage.The integrated voting stage performs self-learning on the weight of the world results of the image feature module and the recognition results of the deepened data extraction module,and uses the output of theintegrated voting stage as the final prediction result.Based on the above research results,this article builds an online vascular infiltration detection system that provides medical vascular infiltration automatic identification services to medical workers.The system is web-based,lightweight,and can be flexibly expanded according to demand.This article has performed experiments on the proposed algorithm.The experimental data includes two parts.One is the CT scan data of the abdomen of patients with hepatocellular tumors,which totals 262 case data.Each case data is drawn by experts to map the tumor lesion area,in which the vascular infiltration.A total of 13,116 tomographic slices were obtained from all CT scans.The other part is the biochemical data detected by the patient's medical indicators.