Abstract
COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The most serious complication of COVID-19 is a type of pneumonia called 2019 novel coronavirus-infected pneumonia (NCIP) with about 4.3% mortality rate. Comparing to chest Digital Radiography (DR), it is recently reported that chest Computed Tomography (CT) is more useful to serve as the early screening and diagnosis tool for NCIP. In this study, aimed to help physicians make the diagnostic decision, we develop a machine learning (ML) approach for automated diagnosis of NCIP on chest CT. Different from most ML approaches which often require training on thousands or millions of samples, we design a few-shot learning approach, in which we combine few-shot learning with weakly supervised model training, for computerized NCIP diagnosis. A total of 824 patients are retrospectively collected from two Hospitals with IRB approval. We first use 9 patients with clinically confirmed NCIP and 20 patients without known lung diseases for training a location detector which is a multitask deep convolutional neural network (DCNN) designed to output a probability of NCIP and the segmentation of targeted lesion area. An experienced radiologist manually localizes the potential locations of NCIPs on chest CTs of 9 COVID-19 patients and interactively segments the area of the NCIP lesions as the reference standard. Then, the multitask DCNN is furtherly fine-tuned by a weakly supervised learning scheme with 291 case-level labeled samples without lesion labels. A test set of 293 patients is independently collected for evaluation. With our NCIP-Net, the test AUC is 0.91. Our system has potential to serve as the NCIP screening and diagnosis tools for the fight of COVID-19’s endemic and pandemic.
Highlights
On December 31, 2019, the cases of pneumonia unknown etiology in Wuhan are firstly reported to WHO China Country Office and subsequently named as COVID-19
Our purpose was to exam whether machine learning could be useful in early diagnosis of novel coronavirus-infected pneumonia (NCIP) caused by COVID-19
The advantage of deep convolutional neural network (DCNN) is that it could generate the characteristic map automatically based on the training samples
Summary
On December 31, 2019, the cases of pneumonia unknown etiology in Wuhan are firstly reported to WHO China Country Office and subsequently named as COVID-19. Its pathogen has been confirmed by multiple studies [1], [2] and more than 100,000 confirmed cases have been reported globally. The associate editor coordinating the review of this manuscript and approving it for publication was Yudong Zhang. As well as normal lifestyle in certain countries especially China. Researchers have been working tirelessly to develop therapeutic drugs or vaccines for the treatment and prevention of COVID-19. There is still a long way for any effective medicine available for public use. The only effective method in clinical practice is to detect and diagnose COVID-19 early to prevent its further spread
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