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Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.
Gao, Kai; Su, Jianpo; Jiang, Zhongbiao; Zeng, Ling-Li; Feng, Zhichao; Shen, Hui; Rong, Pengfei; Xu, Xin; Qin, Jian; Yang, Yuexiang; Wang, Wei; Hu, Dewen.
Afiliação
  • Gao K; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China.
  • Su J; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China.
  • Jiang Z; Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Zeng LL; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China.
  • Feng Z; Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Shen H; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China. Electronic address: shenhui@nudt.edu.cn.
  • Rong P; Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China. Electronic address: rongpengfei66@163.com.
  • Xu X; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China.
  • Qin J; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China.
  • Yang Y; College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China.
  • Wang W; Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Hu D; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China.
Med Image Anal ; 67: 101836, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33129141
ABSTRACT
The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.
Assuntos
Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Pneumonia Viral / Tomografia Computadorizada por Raios X / Redes Neurais de Computação Tipo de estudo: Estudo diagnóstico Limite: Humanos Idioma: Inglês Revista: Med Image Anal Assunto da revista: Diagnóstico por Imagem Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: China

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Pneumonia Viral / Tomografia Computadorizada por Raios X / Redes Neurais de Computação Tipo de estudo: Estudo diagnóstico Limite: Humanos Idioma: Inglês Revista: Med Image Anal Assunto da revista: Diagnóstico por Imagem Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: China
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