{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:42:39Z","timestamp":1742982159465,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031133237"},{"type":"electronic","value":"9783031133244"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13324-4_43","type":"book-chapter","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T20:21:50Z","timestamp":1659558110000},"page":"508-519","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mixup Data Augmentation for COVID-19 Infection Percentage Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5259-7353","authenticated-orcid":false,"given":"Maria Ausilia","family":"Napoli Spatafora","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3461-4679","authenticated-orcid":false,"given":"Alessandro","family":"Ortis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-2470","authenticated-orcid":false,"given":"Sebastiano","family":"Battiato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"43_CR1","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"T Ai","year":"2020","unstructured":"Ai, T., et al.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296, E32\u2013E40 (2020)","journal-title":"Radiology"},{"issue":"9","key":"43_CR2","doi-asserted-by":"publisher","first-page":"189","DOI":"10.3390\/jimaging7090189","volume":"7","author":"F Bougourzi","year":"2021","unstructured":"Bougourzi, F., Distante, C., Ouafi, A., Dornaika, F., Hadid, A., Taleb-Ahmed, A.: Per-COVID-19: a benchmark dataset for COVID-19 percentage estimation from CT-Scans. J. Imaging 7(9), 189 (2021). https:\/\/doi.org\/10.3390\/jimaging7090189","journal-title":"J. Imaging"},{"key":"43_CR3","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s10140-021-01905-6","volume":"28","author":"M Cellina","year":"2021","unstructured":"Cellina, M., Martinenghi, C., Marino, P., Oliva, G.: COVID-19 pneumonia-ultrasound, radiographic, and computed tomography findings: a comprehensive pictorial essay. Emerg. Radiol. 28, 519\u2013526 (2021)","journal-title":"Emerg. Radiol."},{"issue":"2","key":"43_CR4","doi-asserted-by":"publisher","first-page":"E115","DOI":"10.1148\/radiol.2020200432","volume":"296","author":"Y Fang","year":"2020","unstructured":"Fang, Y., et al.: Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296(2), E115\u2013E117 (2020)","journal-title":"Radiology"},{"issue":"5","key":"43_CR5","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1002\/jmv.25699","volume":"92","author":"M Giovanetti","year":"2020","unstructured":"Giovanetti, M., Benvenuto, D., Angeletti, S., Ciccozzi, M.: The first two cases of 2019-nCoV in Italy: where they come from? J. Med. Virol. 92(5), 518\u2013521 (2020)","journal-title":"J. Med. Virol."},{"key":"43_CR6","doi-asserted-by":"publisher","first-page":"8045","DOI":"10.3390\/s21238045","volume":"21","author":"A Gudigar","year":"2021","unstructured":"Gudigar, A., et al.: Role of artificial intelligence in COVID-19 detection. Sensors 21, 8045 (2021). https:\/\/doi.org\/10.3390\/s21238045","journal-title":"Sensors"},{"key":"43_CR7","doi-asserted-by":"publisher","first-page":"104284","DOI":"10.1016\/j.ijmedinf.2020.104284","volume":"144","author":"M Heidari","year":"2020","unstructured":"Heidari, M., Mirniaharikandehei, S., Khuzani, A., 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. Inform. 144, 104284 (2020). https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104284","journal-title":"Int. J. Med. Inform."},{"key":"43_CR8","doi-asserted-by":"publisher","first-page":"5527271","DOI":"10.1155\/2021\/5527271","volume":"2021","author":"A Helwan","year":"2021","unstructured":"Helwan, A., Ma\u2019aitah, M.K.S., Hamdan, H., Ozsahin, D.U., Tuncyurek, O.: Radiologists versus deep convolutional neural networks: a comparative study for diagnosing COVID-19. Comput. Math. Methods Med. 2021, 5527271 (2021). https:\/\/doi.org\/10.1155\/2021\/5527271","journal-title":"Comput. Math. Methods Med."},{"issue":"10223","key":"43_CR9","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223), 497\u2013506 (2020)","journal-title":"Lancet"},{"key":"43_CR10","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1214\/aoms\/1177703732","volume":"35","author":"PJ Huber","year":"1964","unstructured":"Huber, P.J.: Robust estimation of a location parameter. Ann. Math. Stat. 35, 73\u2013101 (1964)","journal-title":"Ann. Math. Stat."},{"key":"43_CR11","doi-asserted-by":"crossref","unstructured":"Jalwana, M.A., Akhtar, N., Bennamoun, M., Mian, A.: CAMERAS: enhanced resolution and sanity preserving class activation mapping for image saliency. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01606"},{"issue":"11","key":"43_CR12","doi-asserted-by":"publisher","first-page":"127","DOI":"10.3390\/jimaging6110127","volume":"6","author":"I Kandel","year":"2020","unstructured":"Kandel, I., Castelli, M., Popovi\u010d, A.: Musculoskeletal images classification for detection of fractures using transfer learning. J. Imaging 6(11), 127 (2020). https:\/\/doi.org\/10.3390\/jimaging6110127","journal-title":"J. Imaging"},{"issue":"4","key":"43_CR13","doi-asserted-by":"publisher","first-page":"262","DOI":"10.7326\/m20-1495","volume":"173","author":"LM Kucirka","year":"2020","unstructured":"Kucirka, L.M., Lauer, S.A., Laeyendecker, O., Boon, D., Lessler, J.: Variation in false-negative rate of reverse transcriptase polymerase chain reaction-based SARS-CoV-2 tests by time since exposure. Ann. Internal Med. 173(4), 262\u2013267 (2020). https:\/\/doi.org\/10.7326\/m20-1495","journal-title":"Ann. Internal Med."},{"key":"43_CR14","doi-asserted-by":"publisher","unstructured":"Liu, S., Deng, W.: Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) (2015). https:\/\/doi.org\/10.1109\/ACPR.2015.7486599","DOI":"10.1109\/ACPR.2015.7486599"},{"key":"43_CR15","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.3390\/jcm9051350","volume":"9","author":"A Maugeri","year":"2020","unstructured":"Maugeri, A., Barchitta, M., Battiato, S., Agodi, A.: Estimation of unreported novel coronavirus (SARS-CoV-2) infections from reported deaths: a susceptible-exposed-infectious-recovered-dead model. J. Clin. Med. 9, 1350 (2020). https:\/\/doi.org\/10.3390\/jcm9051350","journal-title":"J. Clin. Med."},{"issue":"14","key":"43_CR16","doi-asserted-by":"publisher","first-page":"4964","DOI":"10.3390\/ijerph17144964","volume":"17","author":"A Maugeri","year":"2020","unstructured":"Maugeri, A., Barchitta, M., Battiato, S., Agodi, A.: Modeling the novel coronavirus (SARS-CoV-2) outbreak in Sicily, Italy. Int. J. Environ. Res. Public Health 17(14), 4964 (2020). https:\/\/doi.org\/10.3390\/ijerph17144964","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"43_CR17","doi-asserted-by":"publisher","unstructured":"Miko\u0142ajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW) (2018). https:\/\/doi.org\/10.1109\/IIPHDW.2018.8388338","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"43_CR18","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/computers11010010","volume":"11","author":"DR Nayak","year":"2022","unstructured":"Nayak, D.R., Padhy, N., Mallick, P.K., Bagal, D.K., Kumar, S.: Brain tumour classification using noble deep learning approach with parametric optimization through metaheuristics approaches. Computers 11, 10 (2022)","journal-title":"Computers"},{"issue":"1","key":"43_CR19","doi-asserted-by":"publisher","first-page":"e200034","DOI":"10.1148\/ryct.2020200034","volume":"2","author":"MY Ng","year":"2020","unstructured":"Ng, M.Y., et al.: Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol. Cardiothoracic Imaging 2(1), e200034 (2020)","journal-title":"Radiol. Cardiothoracic Imaging"},{"issue":"3","key":"43_CR20","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vis."},{"key":"43_CR21","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"14","author":"F Shi","year":"2020","unstructured":"Shi, F., et al.: Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev. Biomed. Eng. 14, 4\u201315 (2020)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"1","key":"43_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J. Big Data"},{"key":"43_CR23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"5","key":"43_CR24","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.3390\/s21051742","volume":"21","author":"E Vantaggiato","year":"2021","unstructured":"Vantaggiato, E., Paladini, E., Bougourzi, F., Distante, C., Hadid, A., Taleb-Ahmed, A.: Covid-19 recognition using ensemble-CNNs in two new chest x-ray databases. Sensors 21(5), 1742 (2021)","journal-title":"Sensors"},{"key":"43_CR25","unstructured":"World Health Organization: Statement on the second meeting of the International Health Regulations (2005). Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). https:\/\/www.who.int\/news\/item\/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov). Accessed 14 Mar 2022"},{"key":"43_CR26","unstructured":"World Health Organization: WHO Director-General\u2019s remarks at the media briefing on 2019-nCoV on 11 February 2020. https:\/\/www.who.int\/director-general\/speeches\/detail\/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020. Accessed 14 Mar 2022"},{"key":"43_CR27","unstructured":"Wu, R., Yan, S., Shan, Y., Dang, Q., Sun, G.: Deep image: scaling up image recognition. arXiv:abs\/1501.02876 (2015)"},{"key":"43_CR28","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"43_CR29","unstructured":"Zhang, H., Ciss\u00e9, M., Dauphin, Y., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. arXiv:abs\/1710.09412 (2018)"},{"issue":"7","key":"43_CR30","doi-asserted-by":"publisher","first-page":"3532","DOI":"10.1002\/cam4.2233","volume":"8","author":"W Zhao","year":"2019","unstructured":"Zhao, W., et al.: Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning. Cancer Med. 8(7), 3532\u20133543 (2019)","journal-title":"Cancer Med."},{"key":"43_CR31","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Newsam, S.: DenseNet for dense flow. In: 2017 IEEE International Conference on Image Processing (ICIP) (2017)","DOI":"10.1109\/ICIP.2017.8296389"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing. ICIAP 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13324-4_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T19:10:34Z","timestamp":1666465834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13324-4_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031133237","9783031133244"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13324-4_43","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":"4 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"23 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"168","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":"55% - 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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}