New crown diagnosis system based on deep convolutional neural network and multi-instance learning

Abstract

The invention provides a new crown diagnosis system based on a deep convolutional neural network and multi-instance learning, and the system comprises a feature extraction module which packages a CT sequence of a patient, carries out time domain convolution, screens infected slice instances through weak supervised learning, and obtains an infected segment; The system comprises a multi-branch network system configured to input the feature sequence extracted from the CT sequence of the patient into a plurality of parallel branch networks, wherein class activation sequences output by different parallel branch networks are different, so that different infection fragments are positioned, and the integrity of specific case features of the patient is modeled, an attention divergence induced by the weak supervised learning is confronted to enhance the accuracy and robustness of the infected fragment; a multi-instance learning module configured to perform feature fusion of time domain convolution on multi-instance bagging so as to enhance specific case feature expression of the patient; and a gating attention mechanism module configured to perform adaptive instance feature weighted fusion to avoid gradient disappearance in multi-instance learning.

Description

technical field [0001] The invention relates to the technical field of deep learning and medical image processing, in particular to a novel coronavirus diagnosis system based on deep convolutional neural network and multi-instance learning. Background technique [0002] The World Health Organization has declared that the world has been in a state of pandemic since March 11, 2020. As of August 30, 2020, 25.1 million cases of COVID-19 have been recorded. So far, 16.5 million have been cured and 844k patients have died from the infection. COVID-19 is a highly contagious disease that can cause fever, cough, myalgia, headache, and gastrointestinal symptoms in severe cases, and even acute respiratory distress or multiple organ failure (C.Huang et al., 2020 ). Therefore, a rapid and accurate diagnosis of this new deadly disease is critical. Currently, the method of choice for most clinicians is the reverse transcription polymerase chain reaction (RTPCR) test (Xie et al., 2020),...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a new crown diagnosis system based on deep convolutional neural network and multi-instance learning to solve the problem that the existing automatic interpretation of lung CT images based on deep learning usually requires a lot of manual fine labeling for training

Images