CAGAN: Classifier‐augmented generative adversarial networks for weakly‐supervised COVID‐19 lung lesion localisation
The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help early reveal abnormalities indicative of lung disease. Thus, accurate and automatic localisation of lung lesions is particularly important to a...
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Published in | IET computer vision Vol. 18; no. 1; pp. 1 - 14 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
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John Wiley & Sons, Inc
01.02.2024
Wiley |
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ISSN | 1751-9632 1751-9640 |
DOI | 10.1049/cvi2.12216 |
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Abstract | The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help early reveal abnormalities indicative of lung disease. Thus, accurate and automatic localisation of lung lesions is particularly important to assist physicians in rapid diagnosis of COVID‐19 patients. The authors propose a classifier‐augmented generative adversarial network framework for weakly supervised COVID‐19 lung lesion localisation. It consists of an abnormality map generator, discriminator and classifier. The generator aims to produce the abnormality feature map M to locate lesion regions and then constructs images of the pseudo‐healthy subjects by adding M to the input patient images. Besides constraining the generated images of healthy subjects with real distribution by the discriminator, a pre‐trained classifier is introduced to enhance the generated images of healthy subjects to possess similar feature representations with real healthy people in terms of high‐level semantic features. Moreover, an attention gate is employed in the generator to reduce the noise effect in the irrelevant regions of M. Experimental results on the COVID‐19 CT dataset show that the method is effective in capturing more lesion areas and generating less noise in unrelated areas, and it has significant advantages in terms of quantitative and qualitative results over existing methods.
(1) The authors propose an effective classifier‐augmented generative adversarial network framework for COVID‐19 lung lesion localisation, which provides a more accurate feature map indicating the lesion regions. The proposed framework incorporating the pre‐trained classifier enforces the output of the generator to have similar intermediate feature representations (M) with normal people and thus leads to improved precise lesion localisation. (2) The authors construct an L1 norm reconstruction loss and regularisation loss on M, which keep the patient’s lung structure unchanged when lesion location maps are generated. |
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AbstractList | Abstract The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help early reveal abnormalities indicative of lung disease. Thus, accurate and automatic localisation of lung lesions is particularly important to assist physicians in rapid diagnosis of COVID‐19 patients. The authors propose a classifier‐augmented generative adversarial network framework for weakly supervised COVID‐19 lung lesion localisation. It consists of an abnormality map generator, discriminator and classifier. The generator aims to produce the abnormality feature map M to locate lesion regions and then constructs images of the pseudo‐healthy subjects by adding M to the input patient images. Besides constraining the generated images of healthy subjects with real distribution by the discriminator, a pre‐trained classifier is introduced to enhance the generated images of healthy subjects to possess similar feature representations with real healthy people in terms of high‐level semantic features. Moreover, an attention gate is employed in the generator to reduce the noise effect in the irrelevant regions of M. Experimental results on the COVID‐19 CT dataset show that the method is effective in capturing more lesion areas and generating less noise in unrelated areas, and it has significant advantages in terms of quantitative and qualitative results over existing methods. The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help early reveal abnormalities indicative of lung disease. Thus, accurate and automatic localisation of lung lesions is particularly important to assist physicians in rapid diagnosis of COVID‐19 patients. The authors propose a classifier‐augmented generative adversarial network framework for weakly supervised COVID‐19 lung lesion localisation. It consists of an abnormality map generator, discriminator and classifier. The generator aims to produce the abnormality feature map M to locate lesion regions and then constructs images of the pseudo‐healthy subjects by adding M to the input patient images. Besides constraining the generated images of healthy subjects with real distribution by the discriminator, a pre‐trained classifier is introduced to enhance the generated images of healthy subjects to possess similar feature representations with real healthy people in terms of high‐level semantic features. Moreover, an attention gate is employed in the generator to reduce the noise effect in the irrelevant regions of M. Experimental results on the COVID‐19 CT dataset show that the method is effective in capturing more lesion areas and generating less noise in unrelated areas, and it has significant advantages in terms of quantitative and qualitative results over existing methods. (1) The authors propose an effective classifier‐augmented generative adversarial network framework for COVID‐19 lung lesion localisation, which provides a more accurate feature map indicating the lesion regions. The proposed framework incorporating the pre‐trained classifier enforces the output of the generator to have similar intermediate feature representations (M) with normal people and thus leads to improved precise lesion localisation. (2) The authors construct an L1 norm reconstruction loss and regularisation loss on M, which keep the patient’s lung structure unchanged when lesion location maps are generated. The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help early reveal abnormalities indicative of lung disease. Thus, accurate and automatic localisation of lung lesions is particularly important to assist physicians in rapid diagnosis of COVID‐19 patients. The authors propose a classifier‐augmented generative adversarial network framework for weakly supervised COVID‐19 lung lesion localisation. It consists of an abnormality map generator, discriminator and classifier. The generator aims to produce the abnormality feature map M to locate lesion regions and then constructs images of the pseudo‐healthy subjects by adding M to the input patient images. Besides constraining the generated images of healthy subjects with real distribution by the discriminator, a pre‐trained classifier is introduced to enhance the generated images of healthy subjects to possess similar feature representations with real healthy people in terms of high‐level semantic features. Moreover, an attention gate is employed in the generator to reduce the noise effect in the irrelevant regions of M . Experimental results on the COVID‐19 CT dataset show that the method is effective in capturing more lesion areas and generating less noise in unrelated areas, and it has significant advantages in terms of quantitative and qualitative results over existing methods. |
Author | Zhang, Xian Shi, Canghong Luo, Yong Wu, Xi Yan, Zhe Ren, Hongping Mumtaz, Imran Li, Xiaojie Fei, Xin |
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Cites_doi | 10.1007/978-3-030-59719-1_3 10.1109/ISBI48211.2021.9433806 10.32604/cmc.2020.010069 10.1007/s10723‐020‐09513‐3 10.1109/CVPR.2016.90 10.1007/978-3-319-24574-4_28 10.1109/tmi.2020.2995965 10.1007/978-3-030-32239-7_24 10.1109/ICCV.2019.00028 10.1109/CVPR.2016.319 10.1016/j.cmpb.2022.106731 10.1007/978-3-319-10590-1_53 10.1109/CVPR.2018.00685 10.1016/s0140‐6736(20)30211‐7 10.1007/978-3-319-46475-6_43 10.1007/s11263‐015‐0816‐y 10.1109/ICCV.2017.74 10.1007/978-3-319-46723-8_49 10.1109/CVPR.2018.00867 10.1109/tnnls.2019.2892409 10.1109/ICCV.2017.324 10.32604/csse.2023.034172 10.1109/jbhi.2021.3067465 10.1016/j.neucom.2021.06.012 10.1109/jsen.2021.3062442 10.1109/WACV.2018.00097 10.1016/j.cell.2020.08.029 10.1109/ICCV.2017.244 10.1109/CVPR.2017.369 |
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Snippet | The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help... Abstract The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT)... |
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SubjectTerms | Abnormalities Algorithms Artificial intelligence biomedical MRI Computed tomography computer graphics COVID-19 Deep learning Discriminators Disease Feature maps Generative adversarial networks Health care Lesions Localization Lungs Machine learning Medical imaging Methods patient diagnosis Patients Pneumonia Public health Severe acute respiratory syndrome coronavirus 2 |
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Title | CAGAN: Classifier‐augmented generative adversarial networks for weakly‐supervised COVID‐19 lung lesion localisation |
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