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Liu S, Cai T, Tang X, Wang C. MRL-Net: Multi-Scale Representation Learning Network for COVID-19 Lung CT Image Segmentation. IEEE J Biomed Health Inform 2023; 27:4317-4328. [PMID: 37314916 DOI: 10.1109/jbhi.2023.3285936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Accuracy segmentation of COVID-19 lesions in lung CT images can aid patient screening and diagnosis. However, the blurred, inconsistent shape and location of the lesion area poses a great challenge to this vision task. To tackle this issue, we propose a multi-scale representation learning network (MRL-Net) that integrates CNN with Transformer via two bridge unit: Dual Multi-interaction Attention (DMA) and Dual Boundary Attention (DBA). First, to obtain multi-scale local detailed feature and global contextual information, we combine low-level geometric information and high-level semantic features extracted by CNN and Transformer, respectively. Secondly, for enhanced feature representation, DMA is proposed to fuse the local detailed feature of CNN and the global context information of Transformer. Finally, DBA makes our network focus on the boundary features of the lesion, further enhancing the representational learning. Amounts of experimental results show that MRL-Net is superior to current state-of-the-art methods and achieves better COVID-19 image segmentation performance.
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Men K, Li Y, Wang X, Zhang G, Hu J, Gao Y, Han A, Liu W, Han H. Estimate the incubation period of coronavirus 2019 (COVID-19). Comput Biol Med 2023; 158:106794. [PMID: 37044045 PMCID: PMC10062796 DOI: 10.1016/j.compbiomed.2023.106794] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 02/23/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023]
Abstract
COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.
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Affiliation(s)
- Ke Men
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Yihao Li
- The Gabelli School of Business, Fordham University, Lincoln Center, New York, NY, 10023, USA
| | - Xia Wang
- The Air Force Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Guangwei Zhang
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Jingjing Hu
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Yanyan Gao
- Institute for Research on Health Information and Technology, School of Public Health, Xi'an Medical University, Xi'an, Shaanxi, 710021, China
| | - Ashley Han
- The Skyline High School, Ann Arbor, MI, 48103, USA
| | - Wenbin Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Henry Han
- The Laboratory of Data Science and Artificial Intelligence Innovation, Department of Computer Science, School of Engineering and Computer Science, Baylor University, Waco, TX, 76789, USA.
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Luo C, Xu Y, Shao Y, Wang Z, Hu J, Yuan J, Liu Y, Duan M, Huang L, Zhou F. EvaGoNet: an integrated network of variational autoencoder and Wasserstein generative adversarial network with gradient penalty for binary classification tasks. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Sharma BB, Kumar Sharma N, Banshwar A, Malik H, Marquez FPG. Novel approach to design matched digital filter with Abelian group and fuzzy particle swarm optimization vector quantization. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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Thai C, Tran V, Bui M, Nguyen D, Ninh H, Tran H. Real-time masked face classification and head pose estimation for RGB facial image via knowledge distillation. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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