{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:15:02Z","timestamp":1743074102489,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138690"},{"type":"electronic","value":"9783031138706"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13870-6_64","type":"book-chapter","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T09:03:13Z","timestamp":1660467793000},"page":"787-798","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["COVID-19 Classification from Chest X-rays Based on Attention and Knowledge Distillation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8623-0891","authenticated-orcid":false,"given":"Jiaxing","family":"Lv","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5787-0830","authenticated-orcid":false,"given":"Fazhan","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8182-7349","authenticated-orcid":false,"given":"Wenyan","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5985-8023","authenticated-orcid":false,"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5810-8159","authenticated-orcid":false,"given":"Peng","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2826-1873","authenticated-orcid":false,"given":"Yuan","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4633-6012","authenticated-orcid":false,"given":"Ziheng","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"issue":"4","key":"64_CR1","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1111\/irv.12740","volume":"14","author":"X Liu","year":"2020","unstructured":"Liu, X., Zhang, S.: COVID-19: face masks and human-to-human transmission. Influenza Other Respir. Viruses 14(4), 472 (2020)","journal-title":"Influenza Other Respir. Viruses"},{"key":"64_CR2","unstructured":"Organization, WHO: WHO Director-General\u2019s opening remarks at the media briefing on COVID-19-11 March 2020, Geneva, Switzerland (2020)."},{"key":"64_CR3","unstructured":"Organization, WHO: COVID-19 weekly epidemiological update, edn. 84, 22 March 2022 (2022)"},{"issue":"18","key":"64_CR4","first-page":"1843","volume":"323","author":"W Wang","year":"2020","unstructured":"Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., et al.: Detection of SARS-CoV-2 in different types of clinical specimens. JAMA 323(18), 1843\u20131844 (2020)","journal-title":"JAMA"},{"key":"64_CR5","doi-asserted-by":"crossref","unstructured":"Yang, T., Wang, Y.-C., Shen, C.-F., Cheng, C.-M.: Point-of-care RNA-based diagnostic device for COVID-19, vol. 3, p. 165. Multidisciplinary Digital Publishing Institute (2020)","DOI":"10.3390\/diagnostics10030165"},{"issue":"1","key":"64_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12890-020-01286-5","volume":"20","author":"LA Rousan","year":"2020","unstructured":"Rousan, L.A., Elobeid, E., Karrar, M., Khader, Y.: Chest X-ray findings and temporal lung changes in patients with COVID-19 pneumonia. BMC Pulm. Med. 20(1), 1\u20139 (2020)","journal-title":"BMC Pulm. Med."},{"key":"64_CR7","doi-asserted-by":"crossref","unstructured":"Cleverley, J., Piper, J., Jones, M.M.: The role of chest radiography in confirming covid-19 pneumonia. bmj 370 (2020)","DOI":"10.1136\/bmj.m2426"},{"issue":"1","key":"64_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang, L., Lin, Z.Q., Wong, A.: COVID-NET: a tailored deep convolutional neural network design for detection of covid-19 cases from chest X-ray images. Sci. Rep. 10(1), 1\u201312 (2020)","journal-title":"Sci. Rep."},{"key":"64_CR9","unstructured":"Shoeibi, A., Khodatars, M., Alizadehsani, R., Ghassemi, N., Jafari, M., Moridian, P., et al.: Automated detection and forecasting of covid-19 using deep learning techniques: A review. arXiv preprint arXiv:2007.10785 (2020)"},{"key":"64_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105532","volume":"194","author":"RM Pereira","year":"2020","unstructured":"Pereira, R.M., Bertolini, D., Teixeira, L.O., Silla, C.N., Jr., Costa, Y.M.: COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput. Methods Programs Biomed. 194, 105532 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"7","key":"64_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-021-01745-4","volume":"45","author":"K Hammoudi","year":"2021","unstructured":"Hammoudi, K., Benhabiles, H., Melkemi, M., Dornaika, F., Arganda-Carreras, I., Collard, D., et al.: Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the era of COVID-19. J. Med. Syst. 45(7), 1\u201310 (2021). https:\/\/doi.org\/10.1007\/s10916-021-01745-4","journal-title":"J. Med. Syst."},{"issue":"9","key":"64_CR12","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.2174\/092986610791760379","volume":"17","author":"B Wang","year":"2010","unstructured":"Wang, B., Chen, P., Zhang, J., Zhao, G., Zhang, X.: Inferring protein-protein interactions using a hybrid genetic algorithm\/support vector machine method. Protein Pept. Lett. 17(9), 1079\u20131084 (2010)","journal-title":"Protein Pept. Lett."},{"issue":"1","key":"64_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-11-182","volume":"11","author":"B Wang","year":"2010","unstructured":"Wang, B., Valentine, S., Plasencia, M., Raghuraman, S., Zhang, X.: Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry. BMC Bioinf. 11(1), 1\u201311 (2010). https:\/\/doi.org\/10.1186\/1471-2105-11-182","journal-title":"BMC Bioinf."},{"key":"64_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1007\/978-3-642-24553-4_64","volume-title":"Bio-Inspired Computing and Applications","author":"B Wang","year":"2012","unstructured":"Wang, B., Fang, A., Shi, X., Kim, S.H., Zhang, X.: DISCO2: a comprehensive peak alignment algorithm for two-dimensional gas chromatography time-of-flight mass spectrometry. In: Huang, D.-S., Gan, Y., Premaratne, P., Han, K. (eds.) ICIC 2011. LNCS, vol. 6840, pp. 486\u2013491. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-24553-4_64"},{"key":"64_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/978-3-319-22053-6_72","volume-title":"Advanced Intelligent Computing Theories and Applications","author":"P Chen","year":"2015","unstructured":"Chen, P., Hu, S., Wang, B., Zhang, J.: A random projection ensemble approach to drug-target interaction prediction. In: Huang, D.-S., Han, K. (eds.) ICIC 2015. LNCS (LNAI), vol. 9227, pp. 693\u2013699. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-22053-6_72"},{"issue":"3","key":"64_CR16","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","volume":"24","author":"A Narin","year":"2021","unstructured":"Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal. Appl. 24(3), 1207\u20131220 (2021). https:\/\/doi.org\/10.1007\/s10044-021-00984-y","journal-title":"Pattern Anal. Appl."},{"key":"64_CR17","doi-asserted-by":"crossref","unstructured":"Jaiswal, A.K., Tiwari, P., Rathi, V.K., Qian, J., Pandey, H.M., Albuquerque, V.H.C.: COVIDPEN: A novel COVID-19 detection model using chest X-rays and CT scans. Medrxiv (2020)","DOI":"10.1101\/2020.07.08.20149161"},{"key":"64_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101794","volume":"65","author":"S Minaee","year":"2020","unstructured":"Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., Soufi, G.J.: Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med. Image Anal. 65, 101794 (2020)","journal-title":"Med. Image Anal."},{"key":"64_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104284","volume":"144","author":"M Heidari","year":"2020","unstructured":"Heidari, M., Mirniaharikandehei, S., Khuzani, A.Z., Danala, G., Qiu, Y., Zheng, B.: Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int. J. Med. Informatics 144, 104284 (2020)","journal-title":"Int. J. Med. Informatics"},{"key":"64_CR20","unstructured":"Hemdan, E.E.-D., Shouman, M.A., Karar, M.E.: COVIDX-Net: A framework of deep learning classifiers to diagnose covid-19 in X-ray images. ArXiv preprint arXiv:2003.11055 (2020)"},{"key":"64_CR21","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1016\/j.patrec.2020.09.010","volume":"138","author":"P Afshar","year":"2020","unstructured":"Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K.N., Mohammadi, A.: COVID-CAPS: a capsule network-based framework for identification of COVID-19 cases from X-ray images. Pattern Recogn. Lett. 138, 638\u2013643 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"64_CR22","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"ME Chowdhury","year":"2020","unstructured":"Chowdhury, M.E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Mahbub, Z.B., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665\u2013132676 (2020)","journal-title":"IEEE Access"},{"key":"64_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103869","volume":"122","author":"T Mahmud","year":"2020","unstructured":"Mahmud, T., Rahman, M.A., Fattah, S.A.: CovXNet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput. Biol. Med. 122, 103869 (2020)","journal-title":"Comput. Biol. Med."},{"key":"64_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"64_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"64_CR26","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"64_CR27","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint  arXiv2(7):1503.02531 (2015)"},{"key":"64_CR28","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"64_CR29","doi-asserted-by":"crossref","unstructured":"Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Mirjalili, S., Khan, M.K.: Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms\u2019. In: Multimodal Image Exploitation and Learning 2021. International Society for Optics and Photonics, p. 117340E (2021)","DOI":"10.1117\/12.2588672"},{"key":"64_CR30","doi-asserted-by":"crossref","unstructured":"Tangudu, V., Kakarla, J., Venkateswarlu, I.B.: COVID-19 detection from chest X-ray using MobileNet and residual separable convolution block. Soft Comput. 1\u201312 (2022)","DOI":"10.1007\/s00500-021-06579-3"},{"key":"64_CR31","unstructured":"Kaggle COVID-19 radiography database. https:\/\/www.kaggle.com\/tawsifurrahman\/covid19-radiography-database. Accessed 18 May 2022"},{"key":"64_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/978-3-319-42291-6_42","volume-title":"Intelligent Computing Theories and Application","author":"S Hu","year":"2016","unstructured":"Hu, S., Chen, P., Zhang, J., Wang, B.: Prediction of hot spots based on physicochemical features and relative accessible surface area of amino acid sequence. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2016. LNCS, vol. 9771, pp. 422\u2013431. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-42291-6_42"},{"key":"64_CR33","doi-asserted-by":"crossref","unstructured":"Wang, B., Du, L., Zhang, J., Chen, P.: A hierarchical model for identifying mild cognitive impairment. In: 2015 11th International Conference on Natural Computation (ICNC), pp. 599\u2013604. IEEE (2015)","DOI":"10.1109\/ICNC.2015.7378057"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13870-6_64","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:02:13Z","timestamp":1709830933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13870-6_64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138690","9783031138706"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13870-6_64","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2022\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IC-ICC-CN","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"449","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"209","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}