题名 | Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT |
作者 | |
通讯作者 | Zhang, Jingfeng; Zhao, Yitian |
发表日期 | 2021-12-02
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DOI | |
发表期刊 | |
ISSN | 2234-943X
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卷号 | 11 |
摘要 | ObjectiveTo develop an accurate and rapid computed tomography (CT)-based interpretable AI system for the diagnosis of lung diseases. BackgroundMost existing AI systems only focus on viral pneumonia (e.g., COVID-19), specifically, ignoring other similar lung diseases: e.g., bacterial pneumonia (BP), which should also be detected during CT screening. In this paper, we propose a unified sequence-based pneumonia classification network, called SLP-Net, which utilizes consecutiveness information for the differential diagnosis of viral pneumonia (VP), BP, and normal control cases from chest CT volumes. MethodsConsidering consecutive images of a CT volume as a time sequence input, compared with previous 2D slice-based or 3D volume-based methods, our SLP-Net can effectively use the spatial information and does not need a large amount of training data to avoid overfitting. Specifically, sequential convolutional neural networks (CNNs) with multi-scale receptive fields are first utilized to extract a set of higher-level representations, which are then fed into a convolutional long short-term memory (ConvLSTM) module to construct axial dimensional feature maps. A novel adaptive-weighted cross-entropy loss (ACE) is introduced to optimize the output of the SLP-Net with a view to ensuring that as many valid features from the previous images as possible are encoded into the later CT image. In addition, we employ sequence attention maps for auxiliary classification to enhance the confidence level of the results and produce a case-level prediction. ResultsFor evaluation, we constructed a dataset of 258 chest CT volumes with 153 VP, 42 BP, and 63 normal control cases, for a total of 43,421 slices. We implemented a comprehensive comparison between our SLP-Net and several state-of-the-art methods across the dataset. Our proposed method obtained significant performance without a large amount of data, outperformed other slice-based and volume-based approaches. The superior evaluation performance achieved in the classification experiments demonstrated the ability of our model in the differential diagnosis of VP, BP and normal cases. |
关键词 | |
相关链接 | [来源记录] |
收录类别 | |
语种 | 英语
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学校署名 | 其他
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资助项目 | Zhejiang Provincial Natural Science Foundation of China[
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WOS研究方向 | Oncology
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WOS类目 | Oncology
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WOS记录号 | WOS:000730586100001
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出版者 | |
来源库 | Web of Science
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引用统计 |
被引频次[WOS]:4
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成果类型 | 期刊论文 |
条目标识符 | http://kc.sustech.edu.cn/handle/2SGJ60CL/259254 |
专题 | 工学院_计算机科学与工程系 |
作者单位 | 1.Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Cixi Inst Biomed Engn, Ningbo, Peoples R China 2.Univ Chinese Acad Sci, Sch Opt Technol, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Hwa Mei Hosp, Ningbo, Peoples R China 4.Ningbo Univ, Sch Med, Ningbo, Peoples R China 5.Univ Liverpool, Dept Eye & Vis Sci, Liverpool, Merseyside, England 6.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China 7.Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Zhejiang Int Sci & Technol Cooperat Base Biomed M, Ningbo, Peoples R China 8.Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Zhejiang Engn Res Ctr Biomed Mat, Ningbo, Peoples R China |
推荐引用方式 GB/T 7714 |
Hao, Jinkui,Xie, Jianyang,Liu, Ri,et al. Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT[J]. Frontiers in Oncology,2021,11.
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APA |
Hao, Jinkui.,Xie, Jianyang.,Liu, Ri.,Hao, Huaying.,Ma, Yuhui.,...&Zhao, Yitian.(2021).Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT.Frontiers in Oncology,11.
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MLA |
Hao, Jinkui,et al."Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT".Frontiers in Oncology 11(2021).
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