{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:59:47Z","timestamp":1777705187864,"version":"3.51.4"},"reference-count":49,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,9,15]]},"abstract":"<jats:p>Since the end of 2019, the COVID-19, which has swept across the world, has caused serious impacts on public health and economy. Although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for clinical diagnosis, it is very time-consuming and labor-intensive. At the same time, more and more people have doubted the sensitivity of RT-PCR. Therefore, Computed Tomography (CT) images are used as a substitute for RT-PCR. Powered by the research of the field of artificial intelligence, deep learning, which is a branch of machine learning, has made a great success on medical image segmentation. However, general full supervision methods require pixel-level point-by-point annotations, which is very costly. In this paper, we put forward an image segmentation method based on weakly supervised learning for CT images of COVID-19, which can effectively segment the lung infection area and doesn\u2019t require pixel-level labels. Our method is contrasted with another four weakly supervised learning methods in recent years, and the results have been significantly improved.<\/jats:p>","DOI":"10.3233\/jifs-210569","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T13:13:44Z","timestamp":1627391624000},"page":"3265-3276","source":"Crossref","is-referenced-by-count":0,"title":["A weakly supervised learning method based on attention fusion for Covid-19 segmentation in CT images"],"prefix":"10.1177","volume":"41","author":[{"given":"Hongyu","family":"Chen","sequence":"first","affiliation":[{"name":"College of Software, Jilin University, Changchun, China"},{"name":"Key Laboratoryof Symbolic Computation and Knowledge Engineering of Ministry ofEducation, Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Software, Jilin University, Changchun, China"},{"name":"College of Computer Science and Technology, Jilin University, Changchun, China"},{"name":"Key Laboratoryof Symbolic Computation and Knowledge Engineering of Ministry ofEducation, Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-210569_ref1","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1111\/tmi.13383","article-title":"The COVID-19 epidemic","volume":"25","author":"Velavan","year":"2020","journal-title":"Tropical Medicine & International Health"},{"issue":"4","key":"10.3233\/JIFS-210569_ref2","doi-asserted-by":"crossref","first-page":"E15","DOI":"10.1148\/radiol.2020200490","article-title":"Coronavirus disease 2019 (COVID-19): a perspective from China","volume":"296","author":"Zu","year":"2020","journal-title":"Radiology"},{"key":"10.3233\/JIFS-210569_ref3","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Medical Image Analysis"},{"issue":"11","key":"10.3233\/JIFS-210569_ref4","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1001\/jama.2020.1585","article-title":"Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus\u2013infected pneumonia in Wuhan, China","volume":"323","author":"Wang","year":"2020","journal-title":"Jama"},{"issue":"2","key":"10.3233\/JIFS-210569_ref5","doi-asserted-by":"crossref","first-page":"E32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases","volume":"296","author":"Ai","year":"2020","journal-title":"Radiology"},{"issue":"2","key":"10.3233\/JIFS-210569_ref6","doi-asserted-by":"crossref","first-page":"E115","DOI":"10.1148\/radiol.2020200432","article-title":"Sensitivity of chest CT for COVID-19: comparison to RT-PCR","volume":"296","author":"Fang","year":"2020","journal-title":"Radiology"},{"issue":"2","key":"10.3233\/JIFS-210569_ref7","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.radi.2005.02.003","article-title":"How do radiologists do it? The influence of experience and training on searching for chest nodules","volume":"12","author":"Manning","year":"2006","journal-title":"Radiography"},{"key":"10.3233\/JIFS-210569_ref8","doi-asserted-by":"crossref","unstructured":"Chen C. , Liu X. , Ding M. , Zheng J. and Li J. , 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI, International Conference on Medical Image Computing andComputer-Assisted Intervention, 2019, 184\u2013192.","DOI":"10.1007\/978-3-030-32248-9_21"},{"key":"10.3233\/JIFS-210569_ref9","doi-asserted-by":"crossref","unstructured":"Kuang H. , Menon B.K. and Qiu W. , Automated infarct segmentation from follow-up non-contrast CT scans in patients with acute ischemic stroke using dense multi-path contextual generative adversarial network, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019, 856\u2013863.","DOI":"10.1007\/978-3-030-32248-9_95"},{"key":"10.3233\/JIFS-210569_ref10","doi-asserted-by":"crossref","unstructured":"Zhang W. , Li G. , Wang F. , Longjiang E. , Yu Y. , Lin L. and Liang H. , Simultaneous Lung Field Detection and Segmentation for Pediatric Chest Radiographs, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019, 594\u2013602.","DOI":"10.1007\/978-3-030-32226-7_66"},{"key":"10.3233\/JIFS-210569_ref11","doi-asserted-by":"crossref","unstructured":"Fang C. , Li G. , Pan C. , Li Y. and Yu Y. , Globally guided progressive fusion network for 3D pancreas segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019, 210\u2013218.","DOI":"10.1007\/978-3-030-32245-8_24"},{"key":"10.3233\/JIFS-210569_ref14","doi-asserted-by":"crossref","unstructured":"Ronneberger O. , Fischer P. and Brox T. , U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.3233\/JIFS-210569_ref15","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6. , Abdulkadir A. , Lienkamp S.S. , Brox T. and Ronneberger O. , 3DU-Net: learning dense volumetric segmentation from sparse annotation, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016, 424\u2013432.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"10.3233\/JIFS-210569_ref17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Netarchitecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Networks"},{"key":"10.3233\/JIFS-210569_ref18","doi-asserted-by":"crossref","unstructured":"Zhou B. , Khosla A. , Lapedriza A. , Oliva A. and Torralba A. , Learning deep features for discriminative localization, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 2921\u20132929.","DOI":"10.1109\/CVPR.2016.319"},{"key":"10.3233\/JIFS-210569_ref19","doi-asserted-by":"crossref","unstructured":"Jiang P.T. , Hou Q. , Cao Y. , Cheng M.M. , Wei Y. and Xiong H.-K. , Integral object mining via online attention accumulation, Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, 2070\u20132079.","DOI":"10.1109\/ICCV.2019.00216"},{"key":"10.3233\/JIFS-210569_ref20","doi-asserted-by":"crossref","unstructured":"Liu J.J. , Hou Q. , Cheng M.M. , Feng J. and Jiang J. , A simple pooling-based design for real-time salient object detection, Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, 3917\u20133926.","DOI":"10.1109\/CVPR.2019.00404"},{"key":"10.3233\/JIFS-210569_ref22","doi-asserted-by":"crossref","unstructured":"Han K. , Wang Y. , Tian Q. , Guo J. , Xu C. and Xu C. , Ghostnet: More features from cheap operations, Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, 1580\u20131589.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"10.3233\/JIFS-210569_ref23","doi-asserted-by":"crossref","unstructured":"Sandler M. , Howard A. , Zhu M. , Zhmoginov A. and Chen L.-C. , Mobilenetv2: Inverted residuals and linear bottlenecks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.3233\/JIFS-210569_ref24","doi-asserted-by":"crossref","unstructured":"Long J. , Shelhamer E. and Darrell T. , Fully convolutional networks for semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10.3233\/JIFS-210569_ref25","doi-asserted-by":"crossref","unstructured":"Xiao X. , Lian S. , Luo Z. and Li S. , Weighted res-unet for high-quality retina vessel segmentation, International Conference on Information Technology in Medicine and Education (ITME), 2018, 327\u2013331.","DOI":"10.1109\/ITME.2018.00080"},{"issue":"8","key":"10.3233\/JIFS-210569_ref27","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","article-title":"Inf-net: Automatic covid-19 lung infection segmentation from ct images","volume":"39","author":"Fan","year":"2020","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"6","key":"10.3233\/JIFS-210569_ref29","doi-asserted-by":"crossref","first-page":"4846","DOI":"10.1609\/aaai.v35i6.16617","article-title":"MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation","volume":"35","author":"Qiu","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-210569_ref30","doi-asserted-by":"crossref","unstructured":"Dai J. , He K. and Sun J. , Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation, Proceedings of the IEEE International Conference on Computer Vision, 2015, 1635\u20131643.","DOI":"10.1109\/ICCV.2015.191"},{"key":"10.3233\/JIFS-210569_ref31","doi-asserted-by":"crossref","unstructured":"Lin D. , Dai J. , Jia J. , He K. and Sun J. , Scribblesup: Scribble-supervised convolutional networks for semantic segmentation, Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, 2016, 3159\u20133167.","DOI":"10.1109\/CVPR.2016.344"},{"key":"10.3233\/JIFS-210569_ref32","doi-asserted-by":"crossref","unstructured":"Vernaza P. and Chandraker M. , Learning random-walk label propagation for weakly-supervised semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 7158\u20137166.","DOI":"10.1109\/CVPR.2017.315"},{"key":"10.3233\/JIFS-210569_ref33","doi-asserted-by":"crossref","unstructured":"Bearman A. , Russakovsky O. , Ferrari V. and Fei-Fei L. , What\u2019s the point: Semantic segmentation with point supervision, European Conference on Computer Vision, 2016, 549\u2013565.","DOI":"10.1007\/978-3-319-46478-7_34"},{"key":"10.3233\/JIFS-210569_ref34","doi-asserted-by":"crossref","unstructured":"Ahn J. and Kwak S. , Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, 4981\u20134990.","DOI":"10.1109\/CVPR.2018.00523"},{"key":"10.3233\/JIFS-210569_ref35","doi-asserted-by":"crossref","unstructured":"Huang Z. , Wang X. , Wang J. , Liu W. and Wang J. , Weakly-supervised semantic segmentation network with deep seeded region growing, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, 7014\u20137023.","DOI":"10.1109\/CVPR.2018.00733"},{"key":"10.3233\/JIFS-210569_ref36","doi-asserted-by":"crossref","unstructured":"Oh S.J. , Benenson R. , Khoreva A. , Akata Z. , Fritz M. and Schiele B. , Exploiting saliency for object segmentation from image level labels, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 5038\u20135047.","DOI":"10.1109\/CVPR.2017.535"},{"key":"10.3233\/JIFS-210569_ref37","doi-asserted-by":"crossref","unstructured":"Wei Y. , Feng J. , Liang X. , Cheng M.M. , Zhao Y. and Yan S. , Object region mining with adversarial erasing: A simple classification to semantic segmentation approach, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 1568\u20131576.","DOI":"10.1109\/CVPR.2017.687"},{"key":"10.3233\/JIFS-210569_ref38","doi-asserted-by":"crossref","unstructured":"Afshari S. , BenTaieb A. , Mirikharaji Z. and Hamarneh G. , Weakly supervised fully convolutional network for PET lesion segmentation, Medical Imaging 2019: Image Processing, 2019, 10949: International Society for Optics and Photonics, 109491K.","DOI":"10.1117\/12.2512274"},{"key":"10.3233\/JIFS-210569_ref39","doi-asserted-by":"crossref","unstructured":"Wu K. , Du B. , Luo M. , Wen H. , Shen Y. and Feng J. , Weakly supervised brain lesion segmentation via attentional representation learning, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019, 211\u2013219.","DOI":"10.1007\/978-3-030-32248-9_24"},{"key":"10.3233\/JIFS-210569_ref40","doi-asserted-by":"crossref","unstructured":"Laradji I. , Rodriguez P. , Manas O. , Lensink K. , Law M. , Kurzman L. , Parker W. , Vazquez D. and Nowrouzezahrai D. , A weakly supervised consistency-based learning method for covid-19 segmentation in ct images, in Proceedings of the IEEE\/CVFWinter Conference on Applications of Computer Vision, 2021, 2453\u20132462.","DOI":"10.1109\/WACV48630.2021.00250"},{"key":"10.3233\/JIFS-210569_ref41","doi-asserted-by":"crossref","unstructured":"Chollet F. , Xception: Deep learning with depthwise separable convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"10.3233\/JIFS-210569_ref42","doi-asserted-by":"crossref","unstructured":"Milletari F. , Navab N. and Ahmadi S.-A. , V-net: Fully convolutional neural networks for volumetric medical image segmentation, International Conference on 3D Vision (3DV), 2016, 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"key":"10.3233\/JIFS-210569_ref43","doi-asserted-by":"crossref","unstructured":"Salehi S.S.M. , Erdogmus D. and Gholipour A. , Tversky loss function for image segmentation using 3D fully convolutional deep networks, International Workshop on Machine Learning in Medical Imaging, 2017, 379\u2013387.","DOI":"10.1007\/978-3-319-67389-9_44"},{"issue":"2","key":"10.3233\/JIFS-210569_ref44","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1002\/mp.13300","article-title":"AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy","volume":"46","author":"Zhu","year":"2019","journal-title":"Medical Physics"},{"key":"10.3233\/JIFS-210569_ref45","doi-asserted-by":"crossref","unstructured":"Lin T.Y. , Goyal P. , Girshick R. , He K. and Doll\u00e1r P. , Focal loss for dense object detection, Proceedings of the IEEE International Conference on Computer Vision, 2017, 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.3233\/JIFS-210569_ref47","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","article-title":"Wider or deeper: Revisiting the resnet model for visual recognition","volume":"90","author":"Wu","year":"2019","journal-title":"Pattern Recognition"},{"key":"10.3233\/JIFS-210569_ref48","unstructured":"Kingma D.P. and Ba J. , Adam: A Method for Stochastic Optimization, Proceedings of International Conference on Learning Representations, 2015, 1\u201315."},{"key":"10.3233\/JIFS-210569_ref49","doi-asserted-by":"crossref","unstructured":"Roy A. and Todorovic S. , Combining bottom-up, top-down and smoothness cues for weakly supervised image segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 3529\u20133538.","DOI":"10.1109\/CVPR.2017.770"},{"key":"10.3233\/JIFS-210569_ref50","doi-asserted-by":"crossref","unstructured":"Ahn J. , Cho S. and Kwak S. , Weakly supervised learning of instance segmentation with inter-pixel relations, Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, 2209\u20132218.","DOI":"10.1109\/CVPR.2019.00231"},{"key":"10.3233\/JIFS-210569_ref51","doi-asserted-by":"crossref","unstructured":"Fan J. , Zhang Z. , Song C. and Tan T. , Learning integral objects with intra-class discriminator for weakly-supervised semantic segmentation, Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, 4283\u20134292.","DOI":"10.1109\/CVPR42600.2020.00434"},{"key":"10.3233\/JIFS-210569_ref52","doi-asserted-by":"crossref","unstructured":"Wang Y. , Zhang J. , Kan M. , Shan S. and Chen X. , Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation, Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, 12275\u201312284.","DOI":"10.1109\/CVPR42600.2020.01229"},{"key":"10.3233\/JIFS-210569_ref53","doi-asserted-by":"crossref","unstructured":"Zhao H. , Shi J. , Qi X. , Wang X. and Jia J. , Pyramid scene parsing network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 2881\u20132890.","DOI":"10.1109\/CVPR.2017.660"},{"key":"10.3233\/JIFS-210569_ref54","doi-asserted-by":"crossref","unstructured":"Chen L.C. , Zhu Y. , Papandreou G. , Schroff F. and Adam H. , Encoder-decoder with atrous separable convolution for semantic image segmentation, Proceedings of the European Conference on Computer Vision (ECCV), 2018, 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10.3233\/JIFS-210569_ref55","doi-asserted-by":"crossref","unstructured":"Lo S.Y. , Hang H.M. , Chan S.W. and Lin J.J. , Efficient dense modules of asymmetric convolution for real-time semantic segmentation, Proceedings of the ACM Multimedia Asia, 2019, 1\u20136.","DOI":"10.1145\/3338533.3366558"},{"key":"10.3233\/JIFS-210569_ref57","doi-asserted-by":"crossref","unstructured":"Mehta S. , Rastegari M. , Caspi A. , Shapiro L. and Hajishirzi H. , Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation, Proceedings of the European Conference on Computer Vision (ECCV), 2018, 552\u2013568.","DOI":"10.1007\/978-3-030-01249-6_34"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-210569","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:09Z","timestamp":1777455789000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-210569"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":49,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-210569","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,15]]}}}