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ObjectivesTo develop and externally geographically validate a mixed-effects deep learning model to diagnose COVID-19 from computed tomography (CT) imaging following best practice guidelines and assess the strengths and weaknesses of deep learning COVID-19 diagnosis. DesignModel development and external validation with retrospectively collected data from two countries. SettingHospitals in Moscow, Russia, collected between March 1, 2020, and April 25, 2020. The China Consortium of Chest CT Image Investigation (CC-CCII) collected between January 25, 2020, and March 27, 2020. Participants1,110 and 796 patients with either COVID-19 or healthy CT volumes from Moscow, Russia, and China, respectively. Main outcome measuresWe developed a deep learning model with a novel mixed-effects layer to model the relationship between slices in CT imaging. The model was trained on a dataset from hospitals in Moscow, Russia, and externally geographically validated on a dataset from a consortium of Chinese hospitals. Model performance was evaluated in discriminative performance using the area under the receiver operating characteristic (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, calibration performance was assessed using calibration curves, and clinical benefit was assessed using decision curve analysis. Finally, the models decisions were assessed visually using saliency maps. ResultsExternal validation on the large Chinese dataset showed excellent performance with an AUROC of 0.936 (95%CI: 0.910, 0.961). Using a probability threshold of 0.5, the sensitivity, specificity, NPV, and PPV were 0.753 (0.647, 0.840), 0.909 (0.869, 0.940), 0.711 (0.606, 0.802), and 0.925 (0.888, 0.953), respectively. ConclusionsDeep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models. StatementsThe authors do not own any of the patient data, and ethics approval was not needed. The lead author affirms that this manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. Patients and the public were not involved in the study. FundingThis study was funded by EPSRC studentship (No. 2110275), EPSRC Impact Acceleration Account (IAA) funding, and Amazon Web Services. SummaryO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIDeep learning can diagnose diseases from imaging data automatically C_LIO_LIMany studies using deep learning are of poor quality and fail to follow current best practice guidelines for the development and reporting of predictive models C_LIO_LICurrent methods do not adequately model the relationship between slices in CT volumetric data C_LI What this study addsO_LIA novel method to analyse volumetric imaging data composed of slices such as CT images using deep learning C_LIO_LIModel developed following current best-practice guidelines for the development and reporting of prediction models C_LI
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A novel technique for Radon single-pixel imaging with projective sampling, which is based on the theorem of the Radon transform, is proposed. In contrast to current patterns in conventional single-pixel imaging systems, candy-striped patterns called Radon basis patterns, which are produced by projecting the 1D Hadamard functions along different angles, are employed in our proposed technique. Here, the patterns are loaded into a projection system and then illuminated onto an object. The light reflected from the object is detected by a single-pixel detector. An iterative reconstruction method is used to restore the object's 1D projection functions by summing the 1D Hadamard functions and detected intensities. Next, the Radon spectrum of the object is recovered by arranging the 1D projection functions along the projection angle. Finally, the image of the object can be recovered using a filtered back-projection algorithm with the Radon spectrum. Experiments demonstrate that the proposed technique can obtain the information of the Radon spectrum and image of the object. Recognition directly in the Radon spectrum domain, rather than in the image domain, is fast and yields robust and high classification rates. A recognition experiment is performed by detecting the lines in one scene by searching the singular peaks in the Radon spectrum domain. According to the results, the lines in the scene can be easily detected in the Radon spectrum domain. Other shapes can also be detected by the characteristics of those shapes in the Radon spectrum domain.
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A novel technique for the simultaneous fusion, imaging and encryption of multiple objects using a single-pixel detector is proposed. Here, encoded multiplexing patterns are employed to illuminate multiple objects simultaneously. The mixed light reflected from the objects is detected by a single-pixel detector. An iterative reconstruction method is used to restore the fused image by summing the multiplexed patterns and detected intensities. Next, clear images of the objects are recovered by decoding the fused image. We experimentally obtain fused and multiple clear images by utilizing a single-pixel detector to collect the direct and indirect reflected light. Technically, by utilizing the patterns with per-pixel exposure control, multiple objects' information is multiplexed into the detected intensities and then demultiplexed computationally under the single-pixel imaging and compressed sensing schemes. An encryption experiment is performed by setting the multiplexed patterns' encoding as keys.
