Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT
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), w...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:24c2ff35d4f8494fa74dc53f979e7b6e2021-12-02T07:02:52ZAutomatic Sequence-Based Network for Lung Diseases Detection in Chest CT2234-943X10.3389/fonc.2021.781798https://doaj.org/article/24c2ff35d4f8494fa74dc53f979e7b6e2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.781798/fullhttps://doaj.org/toc/2234-943XObjectiveTo 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.Jinkui HaoJinkui HaoJianyang XieRi LiuHuaying HaoYuhui MaYuhui MaKun YanRuirui LiuYalin ZhengJianjun ZhengJiang LiuJiang LiuJingfeng ZhangYitian ZhaoYitian ZhaoYitian ZhaoFrontiers Media S.A.articledeep learningCTCNNConvLSTMlung diseasesNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021) |
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DOAJ |
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deep learning CT CNN ConvLSTM lung diseases Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
| spellingShingle |
deep learning CT CNN ConvLSTM lung diseases Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Jinkui Hao Jinkui Hao Jianyang Xie Ri Liu Huaying Hao Yuhui Ma Yuhui Ma Kun Yan Ruirui Liu Yalin Zheng Jianjun Zheng Jiang Liu Jiang Liu Jingfeng Zhang Yitian Zhao Yitian Zhao Yitian Zhao Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT |
| description |
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. |
| format |
article |
| author |
Jinkui Hao Jinkui Hao Jianyang Xie Ri Liu Huaying Hao Yuhui Ma Yuhui Ma Kun Yan Ruirui Liu Yalin Zheng Jianjun Zheng Jiang Liu Jiang Liu Jingfeng Zhang Yitian Zhao Yitian Zhao Yitian Zhao |
| author_facet |
Jinkui Hao Jinkui Hao Jianyang Xie Ri Liu Huaying Hao Yuhui Ma Yuhui Ma Kun Yan Ruirui Liu Yalin Zheng Jianjun Zheng Jiang Liu Jiang Liu Jingfeng Zhang Yitian Zhao Yitian Zhao Yitian Zhao |
| author_sort |
Jinkui Hao |
| title |
Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT |
| title_short |
Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT |
| title_full |
Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT |
| title_fullStr |
Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT |
| title_full_unstemmed |
Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT |
| title_sort |
automatic sequence-based network for lung diseases detection in chest ct |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/24c2ff35d4f8494fa74dc53f979e7b6e |
| work_keys_str_mv |
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