浏览全部资源
扫码关注微信
Received：29 May 2020，
Revised：2020-7-11，
Accepted：18 July 2020，
Published：16 October 2020
移动端阅览
2020年3月，世界卫生组织（World Health Organization，WHO）宣布新型冠状病毒肺炎（corona virus disease 2019，COVID-19）为世界大流行病，疫情的爆发给世界各地医疗系统带来巨大压力。现有的COVID-19诊断标准是核酸检测阳性，然而核酸检测假阴性率高达17%~25.5%，为避免漏诊，需要采用基于影像学的AI诊断方法筛查大量疑似病例，扼制疾病传播。本综述将回顾疫情爆发数月以来，基于医学影像的新冠肺炎AI辅助诊断的研究成果。首先介绍CT（computed tomography）和X光片的优缺点，以及COVID-19的放射学特征，然后对数据准备、图像分割和分类识别等AI诊断的关键步骤分别进行阐述，最后介绍COVID-19的跟踪和预后（预先对疾病后续发展过程及结果的判断和估计）。本文还整理了部分公开的COVID-19相关数据集，并对数据标注不足的问题提供了弱监督学习和迁移学习等解决方案。实验验证，AI系统诊断COVID-19的敏感性达到97.4%，特异性达到92.2%，优于放射科医生的诊断结果。其中表现尤为突出的是基于语义分割网络检测COVID-19感染区域，由此可以定量分析感染率。AI系统可以辅助医生诊断和治疗COVID-19，提高放射科医生阅读X光片和CT的效率。
In March 2020
the World Health Organization(WHO) declared the new corona virus pneumonia (COVID-19) as a world pandemic
which means that the epidemic has broken out worldwide. The outbreak of COVID-19 threatens the lives and property safety of countless people and brings great pressure to medical systems. The main clinical symptoms of COVID-19 are fever
cough
and fatigue
which may lead to a fatal complication: acute respiratory distress syndrome. The main challenge in inhibiting the spread of this disease is the lack of efficient detection methods. Although reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard for confirming COVID-19
it takes 4-6 h to obtain the results
and the false-negative rate of RT-PCR detection is as high as 17%-25.5%. Therefore
multiple RT-PCR detections at intervals of several days must be performed to confirm the diagnostic result. In addition
RT-PCR reagents are lacking in many severe epidemic areas. By contrast
X-ray and CT(computed tomography) examination equipment have been widely popularized in hospitals. In clinical practice
by combining clinical symptoms and travel history
CT is an efficient and safe method to diagnose COVID-19. Compared with CT
X-ray examination has faster scanning speed and lower radiation amount. Moreover
X-ray and CT images are important tools for doctors to track and observe the condition and evaluate the efficacy. In summary
medical imaging plays a vital role in limiting the spread of viruses and treating COVID-19. During the outbreak of the epidemic
medical imaging-based AI-assisted diagnostic technology has become a popular research direction. Computer-aided diagnostic technology improves the sensitivity and specificity of doctors' diagnosis and is accurate and efficient
which helps rapid diagnosis of a large number of suspicious cases. For example
the out preformed AI-assisted diagnosis system can achieve an accuracy rate comparable to that of radiologists
and it take less than 1 second to perform a diagnosis. The system has been used in 16 hospitals
with more than 1 300 diagnoses performed daily. This article reviews the latest research works on AI-assisted diagnosis of COVID-19 and analyzes and summarizes them on the basis of four aspects: data preparation
image segmentation
diagnosis
and prognosis. First
this article organizes some public data sets to support the AI-assisted diagnostic technology of COVID-19 and provides several solutions to insufficient datasets
such as the human-in-the-loop strategy
which improves the efficiency of data set production. Using transfer learning
weakly supervised or unsupervised learning can reduce model's dependence on the COVID-19 dataset. Second
the semantic segmentation network is also an indispensable part of the intelligent diagnosis of COVID-19. Segmenting the lung region from the original image is a key pre-processing step
which can reduce the calculation amount of subsequent algorithms. The lesion area helps the doctor to track the condition of the disease
and the infection rate can be calculated according to the size of the infected area. U-Net
U-Net++
and attention U-Net are suitable for the segmentation of medical images because of the small number of parameters
which is not easy to overfit. Furthermore
training the semantic segmentation network with the idea of the generative adversarial network (GAN) can improve the Dice coefficient. Third
this article introduces the AI diagnostic system from two aspects of CT images and X-rays. Comparing different diagnostic schemes
the method of diagnosis based on the segmentation images is better than that based on the original images. Among the classification networks
ResNet and VGG19(visual geometry group 19-layer net) perform better. Methods such as GAN
location attention mechanisms
transfer learning
and combining 2D and 3D features can be used to improve accuracy. In addition
clinical information (travel and contact history
white blood cell count
fever
cough
sputum
patient age
and patient gender) can be used as a basis for diagnosis. For example
algorithm D_FF_Conic uses clinical information as a diagnostic basis and has reached an accuracy rate of 90%. Clinicians will consider medical imaging and clinical information in the process of diagnosis
but the current AI diagnostic system cannot integrate multiple types of data for diagnosis. Although some algorithms can fuse the diagnostic results of medical images with the diagnostic results of clinical information
the simple fine-tuned algorithm haven't learned the deep internal connection between different types of data. Fourth
AI technology can also predict high-risk patients on the basis of infection rates and clinical information. Some research predicted the survival rate of COVID-19 patients on the basis of age
syndrome
and infection rate. Such algorithms can help doctors find and treat high-risk patients early
thereby reducing mortality
which is of great significance. This article shows the latest progress of COVID-19's medical imaging-based AI diagnosis. Although some AI-assisted diagnostic systems have been deployed in hospitals to play a practical role
these algorithms still have some problems
such as insufficient training data
a single diagnostic basis
and the ability to distinguish between non-COVID-19 pneumonia and COVID-19.
Ai T, Yang Z L, Hou H Y, Zhan C N, Chen C, Lv W Z, Tao Q, Sun Z Y and Xia L M. 2020. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China:a report of 1 014 cases. Radiology, 296(2):E32-E40[DOI:10.1148/radiol.2020200642]
Allen Institute for AI, Chan Zuckerberg Initiative, Georgetown University's Center for Security and Emerging Technology, Microsoft Research, IBM, National Library of Medicine and The White House Office. 2020a. COVID-19 Open research dataset challenge (CORD-19)[DB/OL].[2020-05-01] . https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
Allen Institute for AI, Chan Zuckerberg Initiative, Georgetown University's Center for Security and Emerging Technology, Microsoft Research, IBM, National Library of Medicine and The White House Office. 2020b. COVID-19 open research dataset[DB/OL].[2020-05-01] . https://www.semanticscholar.org/cord19 https://www.semanticscholar.org/cord19
Alom M Z, Aspiras T, Taha T M and Asari V K. 2019. Skin cancer segmentation and classification with NABLA-N and inception recurrent residual convolutional networks[EB/OL].[2020-05-01] . https://arxiv.org/pdf/1904.11126.pdf https://arxiv.org/pdf/1904.11126.pdf
Alom M Z, Rahman M M S, Nasrin M S, Taha T M and Asari V K. 2020. COVID_MTNet: COVID-19 detection with multi-task deep learning approaches[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2004.03747.pdf https://arxiv.org/pdf/2004.03747.pdf
Alqudah A M and Qazan S. 2020a. Augmented COVID-19 X-ray images dataset[DB/OL].[2020-05-01] . http://dx.doi.org/10.17632/2fxz4px6d8.4 http://dx.doi.org/10.17632/2fxz4px6d8.4
Alqudah A M, Qazan S and Alqudah A. 2020b. Automated systems for detection of COVID-19 using chest X-ray images and lightweight convolutional neural networks[EB/OL].[2020-05-01] . https://europepmc.org/article/ppr/ppr155593 https://europepmc.org/article/ppr/ppr155593
Bai H X, Wang R, Xiong Z, Hsieh B, Chang K, Halsey K, Tran T M L, Choi J W, Wang D C, Shi L B, Mei J, Jiang X L, Pan I, Zeng Q H, Hu P F, Li Y H, Fu F X, Huang R Y, Sebro R, Yu Q Z, Atalay M K and Liao W H. 2020. AI augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other etiologyon chest CT. Radiology[EB/OL] .[2020-05-01]. https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020201491 https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020201491 [ DOI:10.1148/radiol.2020201491 http://dx.doi.org/10.1148/radiol.2020201491 ].
Barstugan M, Ozkaya U and Ozturk S. 2020. Coronavirus (COVID-19) classification using CT images by machine learning methods[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2003.09424.pdf https://arxiv.org/pdf/2003.09424.pdf
Born J, Brändle G, Cossio M, Disdier M, Goulet J, Roulin J and Wiedemann N. 2020. POCOVID-Net: automatic detection of COVID-19 from a new lung ultrasound imaging dataset (POCUS)[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2004.12084.pdf https://arxiv.org/pdf/2004.12084.pdf
Butt C, Gill J, Chun D and Babu B A. 2020. Deep learning system to screen coronavirus disease 2019 pneumonia. Applied Intelligence.[EB/OL].[2020-05-01] . https://doi.org/10.1007/s10489-020-01714-3 https://doi.org/10.1007/s10489-020-01714-3 [ DOI:10.1007/s10489-020-01714-3 https://doi.org/10.1007/s10489-020-01714-3 ].
Chen J, Wu L L, Zhang J, Zhang L, Gong D X, Zhao Y L, Hu S, Wang Y G, Hu X, Zheng B Q, Zhang K, Wu H L, Dong Z H, Xu Y M, Zhu Y J, Chen X, Yu L L and Yu H G. 2020a. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study[EB/OL].[2020-05-01] . https://www.medrxiv.org/content/10.1101/2020.02.25.20021568v2 https://www.medrxiv.org/content/10.1101/2020.02.25.20021568v2
Chen N S, Zhou M, Dong X, Qu J M, Gong F Y, Han Y, Qiu Y, Wang J L, Liu Y, Wei Y, Xia J A, Yu T, Zhang X X and Zhang L. 2020b. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China:a descriptive study. The Lancet, 395(10223):507-513[DOI:10.1016/S0140-6736(20)30211-7]
Chen Q Y, Allot A and Lu Z Y. 2020c. Keep up with the latest coronavirus research. Nature, 579(7798):#193[DOI:10.1038/d41586-020-00694-1]
Chen Y P, Li J N, Xiao H X, Jin X J, Yan S C and Feng J S. 2017. Dual path networks//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc: 4470-4478
Chowdhury M E H, Rahman T, Khandakar A, Mazhar R, Kadir M A, Mahbub Z B, Islam K R, Khan M S, Iqbal A, Al-Emadi N, Reaz M B I and Islam T I. 2020. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access, 8:132665-132676[DOI:10.1109/ACCESS.2020.3010287]
Chung M, Bernheim A, Mei X Y, Zhang N, Huang M Q, Zeng X J, Cui J F, Xu W J, Yang Y, Fayad Z A, Jacobi A, Li K W, Li S L and Shan H. 2020. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 295(1):202-207[DOI:10.1148/radiol.2020200230]
Cohen J P, Morrison P and Dao L. 2020. COVID-19 image data collection[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2003.11597.pdf https://arxiv.org/pdf/2003.11597.pdf
Gaál G, Maga B and Lukács A. 2020. Attention U-Net based adversarial architectures for chest X-ray lung segmentation[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2003.10304.pdf https://arxiv.org/pdf/2003.10304.pdf
Goodwin B D, Jaskolski C, Zhong C and Asmani H. 2020. Intra-model variability in COVID-19 classification using chest X-ray images[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2005.02167.pdf https://arxiv.org/pdf/2005.02167.pdf
Gozes O, Frid-Adar M, Greenspan H, Browning P D, Zhang H Q, Ji W B, Bernheim A and Siegel E. 2020. Rapid AI development cycle for the coronavirus (COVID-19) pandemic: initial results for automated detection and patient monitoring using deep learning CT image analysis[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2003.05037.pdf https://arxiv.org/pdf/2003.05037.pdf
Hammoudi K, Benhabiles H, Melkemi M, Dornaika F, Arganda-Carreras I, Collard D and Scherpereel A. 2020. Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the era of COVID-19[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2004.03399.pdf https://arxiv.org/pdf/2004.03399.pdf
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE: 770-778[ DOI:10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
He X H, Yang X Y, Zhang S H, Zhao J Y, Zhang Y C, Xing E and Xie P T. 2020. Sample-efficient deep learning for COVID-19 diagnosis based on CT scans[EB/OL].[2020-05-01] . https://www.medrxiv.org/content/10.1101/2020.04.13.20063941v1 https://www.medrxiv.org/content/10.1101/2020.04.13.20063941v1
Hemdan E E D, Shouman M A and Karar M E. 2020.Covidx-Net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2003.11055.pdf https://arxiv.org/pdf/2003.11055.pdf
Hu S P, Gao Y, Niu Z M, Jiang Y H, Li L, Xiao X L, Wang M H, Fang E F, Menpes-Smith W, Xia J, Ye H and Yang G. 2020. Weakly supervised deep learning for COVID-19 infection detection and classification from CT images[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2004.06689.pdf https://arxiv.org/pdf/2004.06689.pdf
Huang C L, Wang Y M, Li X W, Ren L L, Zhao J P, Hu Y, Zhang L, Fan G H, Xu J Y, Gu X Y, Cheng Z S, Yu T, Xia J A, Wei Y, Wu W J, Xie X L, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J G, Wang G F, Jiang R M, Gao Z C, Jin Q, Wang J W and Cao B. 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223):497-506[DOI:10.1016/S0140-6736(20)30183-5]
Jiang X G, Coffee M, Bari A, Wang J Z, Jiang X Y, Huang J P, Shi J C, Dai J Y, Cai J, Zhang T X, Wu Z X, He G Q and Huang Y T. 2020. Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Computers, Materials and Continua, 63(1):537-551[DOI:10.32604/cmc.2020.010691]
Jin S, Wang B, Xu H B, Luo C, Wei L, Zhao W, Hou X X, Ma W S, Xu Z Q, Zheng Z Z, Sun W B, Lan L, Zhang W, Mu X D, Shi C X, Wang Z X, Lee J, Jin Z J, Lin M G, Jin H B, Zhang L, Guo J, Zhao B Q, Ren Z Z, Wang S H, You Z, Dong J H, Wang X H, Wang J M and Xu W. 2020. AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks[EB/OL].[2020-05-01] . https://www.medrxiv.org/content/10.1101/2020.03.19.20039354v1 https://www.medrxiv.org/content/10.1101/2020.03.19.20039354v1
Kanne J P. 2020. Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China:key points for the radiologist. Radiology, 295(1):16-17[DOI:10.1148/radiol.2020200241]
Kermany D, Zhang K and Goldbaum M. 2018. Labeled optical coherence tomography (OCT) and chest X-ray images for classification[DB/OL].[2020-05-01] . https://data.mendeley.com/datasets/rscbjbr9sj/2 https://data.mendeley.com/datasets/rscbjbr9sj/2
Khobahi S, Agarwal C and Soltanalian M. 2020. CoroNet: a deep network architecture for semi-supervised task-based identification of COVID-19 from chest X-Ray images[EB/OL].[2020-05-01] . https://www.medrxiv.org/content/10.1101/2020.04.14.20065722v1 https://www.medrxiv.org/content/10.1101/2020.04.14.20065722v1
Langer T, Favarato M, Giudici R, Bassi G, Garberi R, Villa F, Gay H, Zeduri A, Bragagnolo S, Molteni A, Beretta A, Corradin M, Moreno M, Vismara C, Perno C F, Buscema M, Grossi E and Fumagalli R. 2020. Use of machine learning to rapidly predict positivity to severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) using basic clinical data[EB/OL].[2020-05-01] . https://www.researchsquare.com/article/rs-38576/v1 https://www.researchsquare.com/article/rs-38576/v1
Li L, Qin L X, Xu Z G, Yin Y B, Wang X, Kong B, Bai J J, Lu Y, Fang Z H, Song Q, Cao K L, Liu D L, Wang G S, Xu Q Z, Fang X S, Zhang S Q, Xia J and Xia J. 2020a. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT:evaluation of the diagnostic accuracy. Radiology, 296(2):E65-E71[DOI:10.1148/radiol.2020200905]
Li Z, Zhong Z, Li Y, Zhang T Y, Gao L X, Jin D K, Sun Y, Ye X H, Yu L, Hu Z Y, Xiao J, Huang L Y and Tang Y L. 2020b. From community acquired pneumonia to COVID-19: a deep learning based method for quantitative analysis of COVID-19 on thick-section CT scans[EB/OL].[2020-05-01] . https://www.medrxiv.org/content/10.1101/2020.04.17.20070219v1 https://www.medrxiv.org/content/10.1101/2020.04.17.20070219v1
Lu H Z, Stratton C W and Tang Y W. 2020. Outbreak of pneumonia of unknown etiology in Wuhan, China:the mystery and the miracle. Journal of Medical Virology, 92(4):401-402[DOI:10.1002/jmv.25678]
Maghdid H S, Asaad A T, Ghafoor K Z, Sadiq A S and Khan M K. 2020a. Diagnosing COVID-19 pneumonia from X-Ray and CT images using deep learning and transfer learning algorithms[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2004.00038.pdf https://arxiv.org/pdf/2004.00038.pdf
Maghdid H S, Ghafoor K Z, Sadiq A S, Curran K, Rawat D B and Rabie K. 2020b. A novel AI-enabled framework to diagnose coronavirus COVID-19 using smartphone embedded sensors: design study[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2003.07434.pdf https://arxiv.org/pdf/2003.07434.pdf
MedSeg. 2020. COVID-19 CT segmentation dataset[DB/OL].[2020-05-22] . http://medicalsegmentation.com/covid19/ http://medicalsegmentation.com/covid19/
Mei X Y, Lee H C, Diao K Y, Huang M Q, Lin B, Liu C Y, Xie Z Y, Ma Y X, Robson P M, Chung M, Bernheim A, Mani V, Calcagno C, Li K W, Li S L, Shan H, Lv J, Zhao T T, Xia J L, Long Q H, Steinberger S, Jacobi A, Deyer T, Luksza M, Liu F, Little B P, Fayad Z A and Yang Y. 2020. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nature Medicine.[EB/OL].[2020-05-01] . https://doi.org/10.1038/s41591-020-0931-3 https://doi.org/10.1038/s41591-020-0931-3 [ DOI:10.1038/s41591-020-0931-3 https://doi.org/10.1038/s41591-020-0931-3 ].
Narin A, Kaya C and Pamuk Z. 2020. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks[EB/OL] .[2020-05-18]. https://arxiv.org/pdf/2003.10849.pdf https://arxiv.org/pdf/2003.10849.pdf
National Health Commission of the People's Republic of China, National Administration of Traditional Chinese Medicine. 2020. New coronavirus pneumonia diagnosis and treatment plan (Trial Version 5 Revised)[EB/OL].[2020-05-18] . http://www.nhc.gov.cn/xcs/zhengcwj/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml http://www.nhc.gov.cn/xcs/zhengcwj/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml .
国家卫生健康委办公厅, 国家中医药管理局办公室. 2020.新型冠状病毒肺炎诊疗方案(试行第五版修正版)[EB/OL].[2020-05-01] . http://www.nhc.gov.cn/xcs/zhengcwj/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml http://www.nhc.gov.cn/xcs/zhengcwj/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml
Ng M Y, Lee E Y, Yang J, Yang F F, Li X, Wang H X, Lui M M S, Lo S Y, Leung B, Khong P L, Hui C K M, Yuen K Y and Kuo M D. 2020. Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiology: Cardiothoracic Imaging[EB/OL].[2020-05-01] . https://pubs.rsna.org/doi/10.1148/ryct.2020200034 https://pubs.rsna.org/doi/10.1148/ryct.2020200034 [ DOI:10.1148/ryct.2020200034 http://dx.doi.org/10.1148/ryct.2020200034 ].
Shan F, Gao Y Z, Wang J, Shi W Y, Shi N N, Han M F, Xue Z, Shen D G and Shi Y X. 2020. Lung infection quantification of COVID-19 in CT images with deep learning[EB/OL].[2020-05-01] . https://arxiv.org/pdf/2003.04655.pdf https://arxiv.org/pdf/2003.04655.pdf
Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu K I, Matsui M, Fujita H, Kodera Y and DoiK. 2000. Development of a digital image database for chest radiographs with and without a lung nodule:receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. American Journal of Roentgenology, 174(1):71-74[DOI:10.2214/ajr.174.1.1740071]
Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z. 2016. Rethinking the inception architecture for computer vision//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 2818-2826[ DOI:10.1109/CVPR.2016.308 http://dx.doi.org/10.1109/CVPR.2016.308 ]
The British Society of Thoracic Imaging (BSTI). 2020. COVID-19 imaging database[DB/OL].[2020-05-22] . https://www.bsti.org.uk/training-and-education/covid-19-bsti-imaging-database/ https://www.bsti.org.uk/training-and-education/covid-19-bsti-imaging-database/
Wang F, Jiang M Q, Qian C, Yang S, Li C, Zhang H G, Wang X G, Tang X O. 2017. Residual attention network for image classification//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 3156-3164[ DOI:10.1109/CVPR.2017.683 http://dx.doi.org/10.1109/CVPR.2017.683 ]
Wang L D and Wong A. 2020a. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-Ray images[EB/OL].[2020-05-13] . https://arxiv.org/pdf/2003.09871.pdf https://arxiv.org/pdf/2003.09871.pdf
Wang S, Kang B, Ma J L, Zeng X J, Xiao M M, Guo J, Cai M J, Yang J Y, Li Y D, Meng X F and Xu B. 2020. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)[EB/OL].[2020-05-24] . https://www.medrxiv.org/content/10.1101/2020.02.14.20023028v5 https://www.medrxiv.org/content/10.1101/2020.02.14.20023028v5
Wong A, Shafiee M J, Chwyl B and Li F. 2020. Ferminets: learning generative machines to generate efficient neural networks via generative synthesis[EB/OL].[2020-05-14] . https://arxiv.org/pdf/1809.05989.pdf https://arxiv.org/pdf/1809.05989.pdf
World Health Organization (WHO). 2020a. WHO Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020[EB/OL].[2020-05-10] . https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020 https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020
World Health Organization (WHO). 2020b. Global research on coronavirus disease (COVID-19)[DB/OL].[2020-05-24] . https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov
Wu W, Li X K, Du P, Lang G J, Xu M, Xu K J and Li L J. 2019. A deep learning system that generates quantitative CT reports for diagnosing pulmonary tuberculosis[EB/OL].[2020-05-13] . https://arxiv.org/pdf/1910.02285.pdf https://arxiv.org/pdf/1910.02285.pdf
Yan L, Zhang H T, Goncalves J, Xiao Y, Wang M L, Guo Y Q, Sun C, Tang X C, Jin L, Zhang M Y, Huang X, Xiao Y, Cao H S, Chen Y Y, Ren T X, Wang F, Xiao Y R, Huang S F, Tan X, Huang N N, Jiao B, Zhang Y, Luo A L, Mombaerts L, Jin J Y, Cao Z G, Li S S, Xu H and Yuan Y. 2020. A machine learning-based model for survival prediction in patients with severe COVID-19 infection[EB/OL].[2020-05-18] . https://www.medrxiv.org/content/10.1101/2020.02.27.20028027v3 https://www.medrxiv.org/content/10.1101/2020.02.27.20028027v3
Zhang K, Liu X H, Shen J, Li Z H, Sang Y, Wu X W, Zha Y F, Liang W H, Wang C D, Wang K, Ye L S, Gao M, Zhou Z G, Li L, Wang J, Yang Z H, Cai H M, Xu J, Yang L, Cai W J, Xu W Q, Wu S X, Zhang W, Jiang S P, Zheng L H, Zhang X, Wang L, Lu L, Li J M, Yin H P, Wang W, Li O L, Zhang C, Liang L, Wu T, Deng R Y, Wei K, Zhou Y, Chen T, Lau J Y N,Fok M, He J X, Lin T X, Li W M and Wang G Y. 2020a. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell, 181(6):1423-1433[DOI:10.1016/j.cell.2020.04.045]
Zhang Y F, Niu S C, Qiu Z, Wei Y, Zhao P L, Yao J H, Huang J Z, Wu Q Y and Tan M K. 2020b. COVID-DA: deep domain adaptation from typical pneumonia to COVID-19[EB/OL].[2020-05-06] . https://arxiv.org/pdf/2005.01577.pdf https://arxiv.org/pdf/2005.01577.pdf
Zheng C S, Deng X B, Fu Q, Zhou Q, Feng J P, Ma H, Liu W Y and Wang X G. 2020. Deep learning-based detection for COVID-19 from chest CT using weak label[EB/OL].[2020-05-14] . https://www.medrxiv.org/content/10.1101/2020.03.12.20027185v2 https://www.medrxiv.org/content/10.1101/2020.03.12.20027185v2
Zhou Z W, Siddiquee M R, Tajbakhsh N and Liang J M. 2018. UNet++: a nested U-Net architecture for medical image segmentation//Proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Granada, Spain: Springer: 3-11[ DOI:10.1007/978-3-030-00889-5_1 http://dx.doi.org/10.1007/978-3-030-00889-5_1 ]
相关文章
相关作者
相关机构
京公网安备11010802024621
