Pancreatic tumor pathology automatic diagnosis system based on deep neural network

Abstract

The invention discloses a pancreatic tumor pathology automatic diagnosis system based on a deep neural network, and the system comprises a deep neural network model which comprises a feature extraction network which is used for abstracting the image features of an inputted whole image and generating a convolution feature map; the region generation network is used for screening according to the convolution feature map output by the feature extraction network and recommending candidate regions; the pooling layer is used for carrying out convolution operation on the convolution feature map and the candidate region and converting input signals with different sizes into output signals with fixed lengths to obtain a group of low-dimensional feature vectors; and the classification and regressionlayer is used for constructing a radiomics model for predicting the pathological grade, and predicting the pathological degree of the pancreatic tumor according to the low-dimensional feature vector output by the pooling layer and radiology features. The system can replace a doctor to predict the pathological degree of the pancreatic tumor, manual operation can be reduced, and the processing speedis high.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an automatic pathological diagnosis system for pancreatic tumors based on a deep neural network. Background technique [0002] The pancreas is a retroperitoneal organ with a deep anatomical location and complex surrounding structures, making it difficult to diagnose. With the continuous development and improvement of imaging technology in recent years, it plays an important role in the diagnosis, staging and prognosis of pancreatic cancer. In particular, CT has high spatial resolution and density resolution without overlapping anatomical structures. The most important imaging method for pancreatic cancer. [0003] In traditional diagnosis, professional physicians observe the imaging images, compare and analyze a series of images of cases, and rely on experience to predict the pathological degree of pancreatic tumors. This method requires professional doctors to perfor...

AI Technical Summary

Problems solved by technology

This method requires professional doctors to perform cumbersome manual operations on a large amount of data. At the same time, the accuracy and reliability of the diagnostic results of this method are heavily dependent on the doctor's experience, knowledge and professional quality, and the accuracy of the diagnostic results is limited.

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