Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

By C. Zheng, Xianbo Deng, Qing Fu, Qiang-feng Zhou, Jiapei Feng, Hui Ma, Xinggang Wang. 2020

614 citations recorded by the source corpus.

Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning-based software system was developed using 3D CT volumes to detect COVID-19. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious. 499 CT volumes collected from Dec. 13, 2019, to Jan. 23, 2020, were used for training and 131 CT volumes collected from Jan 24, 2020, to Feb 6, 2020, were used for testing. The deep learning algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. There was an operating point with 0.907 sensitivity and 0.911 specificity in the ROC curve. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training. The easily-trained and high-performance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at \url{https://github.com/sydney0zq/covid-19-detection}.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020, neutral): “961 [15] CT 3D No 542 630 - 313* 229* 131* 3D 90.”
Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature (2020, methodology): “In particular, within the reviewed articles, we identified numerous suggestions of the use of AI-powered tools for the diagnosis and screening of SARS-CoV-2 or COVID-19, as reported in Table 4 [20,22,23,25,26,28,30,32,33, 38,48,50,54,61,69,70,77,78,94,100,108,109,122,130].”
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020, methodology): “The popular segmentation networks for COVID-19 include classic U-Net [51]–[56], UNet++ [57], [58], VB-Net [59].”
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans (2020, methodology): “proposed a 3D deep CNN (DeCoVNet) to detect COVID-19 [5] using chest CT slices.”
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays (2020, neutral): “Zheng et al. [46] proposed a model to detect COVID-19 from CT images by reaching an accuracy equal to 0.9, while Xu et al. [37] achieved an accuracy of 0.86 in detecting COVID-19 considering the ResNet by analisying CT images.”
The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, methodology): “Zheng et al. [77] used UNet [83] to segment the lung area automatically, and then used 3DResNet for classiﬁcation.”
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (2020, methodology): “A combined U-Net segmentation and 3D classiﬁcation CNN has been used to accurately predict the presence of COVID-19 with an accuracy of 90% using a non-public dataset of CT images [63].”
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “A number of studies have proposed deep learning systems based on chest CT imaging to distinguish COVID-19 cases from non-COVID-19 cases (which may include both normal and abnormal cases) [16, 20, 21, 22, 23, 24, 25, 26, 27, 17, 28, 29, 30].”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, positive): “[48] proposed a three-dimensional deep CNN model to detect COVID-19 from CT imagery and reported a 90.”
FLOP: Federated Learning on Medical Datasets using Partial Networks (2021, background): “As communities and organizations across the world continue making efforts to control the pandemic, researchers seek to quicken COVID-19 early detection by automatically classifying computed tomography (CT) scan slices (images) of patients’ chests [7, 20, 29, 30, 40].”
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches (2020, methodology): “Overall, this model showed around 90% accuracy [16].”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, positive): “Zheng et al. [37] developed a novel deep learning model and proposed model was tested with 499 CT images.”
COVID-CT-Dataset: A CT Scan Dataset about COVID-19 (2020, methodology): “proposed a 3D deep CNN to detect COVID-19 (Zheng et al., 2020) using chest CT slices.”