{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:40:23Z","timestamp":1782862823718,"version":"3.54.5"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030863395","type":"print"},{"value":"9783030863401","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86340-1_47","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T12:03:14Z","timestamp":1631275394000},"page":"587-598","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["COViT-GAN: Vision Transformer forCOVID-19 Detection in CT Scan Imageswith Self-Attention GAN forDataAugmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6920-1896","authenticated-orcid":false,"given":"Ara Abigail E.","family":"Ambita","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2248-4328","authenticated-orcid":false,"given":"Eujene Nikka V.","family":"Boquio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7140-1707","authenticated-orcid":false,"suffix":"Jr.","given":"Prospero C.","family":"Naval","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"47_CR1","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":"47_CR2","doi-asserted-by":"crossref","unstructured":"Do, C., Vu, L.: An approach for recognizing covid-19 cases using convolutional neural networks applied to CT scan images. In: Applications of Digital Image Processing XLIII, vol. 11510, p. 1151034. International Society for Optics and Photonics (2020)","DOI":"10.1117\/12.2576276"},{"key":"47_CR3","unstructured":"Dosovitskiy, A., et al.: An image is worth 16\u00a0$$\\times $$\u00a016 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"47_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"47_CR5","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. arXiv preprint arXiv:1706.08500 (2017)"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"47_CR7","doi-asserted-by":"crossref","unstructured":"Ismael, A.M., \u015eeng\u00fcr, A.: Deep learning approaches for covid-19 detection based on chest x-ray images. Expert Syst. Appl. 164, 114054 (2020)","DOI":"10.1016\/j.eswa.2020.114054"},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Chen, H., Loew, M., Ko, H.: Covid-19 CT image synthesis with a conditional generative adversarial network. IEEE J. Biomed. Health Inf. (2020)","DOI":"10.1109\/JBHI.2020.3042523"},{"key":"47_CR9","unstructured":"Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., Elghamrawy, S.: Detection of coronavirus (covid-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest x-ray dataset. arXiv preprint arXiv:2004.01184 (2020)"},{"key":"47_CR10","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: CT imaging changes of corona virus disease 2019(COVID-19): a multi-center study in Southwest China. J. Transl. Med. 18(1), 154 (2020)","DOI":"10.1186\/s12967-020-02324-w"},{"key":"47_CR11","doi-asserted-by":"crossref","unstructured":"Loey, M., Manogaran, G., Khalifa, N.E.M.: A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Comput. Appl. (2020)","DOI":"10.20944\/preprints202004.0252.v1"},{"key":"47_CR12","doi-asserted-by":"crossref","unstructured":"Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Khan, M.K.: Diagnosing covid-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms. arXiv preprint arXiv:2004.00038 (2020)","DOI":"10.1117\/12.2588672"},{"key":"47_CR13","doi-asserted-by":"crossref","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)","DOI":"10.1016\/j.compbiomed.2020.103869"},{"key":"47_CR14","unstructured":"Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., Malossi, C.: BAGAN: data augmentation with balancing GAN. arXiv preprint arXiv:1803.09655 (2018)"},{"key":"47_CR15","unstructured":"Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: International Conference on Machine Learning, pp. 2642\u20132651. PMLR (2017)"},{"key":"47_CR16","doi-asserted-by":"crossref","unstructured":"Polsinelli, M., Cinque, L., Placidi, G.: A light CNN for detecting covid-19 from CT scans of the chest. arXiv preprint arXiv:2004.12837 (2020)","DOI":"10.1016\/j.patrec.2020.10.001"},{"key":"47_CR17","doi-asserted-by":"crossref","unstructured":"Shi, H., et al.: Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect. Dis. 20(4), 425\u2013434 (2020)","DOI":"10.1016\/S1473-3099(20)30086-4"},{"key":"47_CR18","doi-asserted-by":"crossref","unstructured":"Silva, P., et al.: COVID-19 detection in CT images with deep learning: a voting-based scheme and cross-datasets analysis. Inf. Med. Unlocked 20, 100427 (2020)","DOI":"10.1016\/j.imu.2020.100427"},{"key":"47_CR19","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"47_CR20","doi-asserted-by":"publisher","unstructured":"Soares, E., Angelov, P., Biaso, S., Higa Froes, M., Kanda Abe, D.: SARS-CoV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-CoV-2 identification. preprint, Health Informatics, April 2020. https:\/\/doi.org\/10.1101\/2020.04.24.20078584","DOI":"10.1101\/2020.04.24.20078584"},{"key":"47_CR21","doi-asserted-by":"crossref","unstructured":"Sultan, O.M., et al.: Pulmonary CT manifestations of COVID-19: changes within 2 weeks duration from presentation. Egyptian J. Radiol. Nuclear Med. 51(1), 105, December 2020","DOI":"10.1186\/s43055-020-00223-0"},{"key":"47_CR22","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 843\u2013852 (2017)","DOI":"10.1109\/ICCV.2017.97"},{"key":"47_CR23","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019)"},{"key":"47_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"key":"47_CR25","doi-asserted-by":"crossref","unstructured":"Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., Pinheiro, P.R.: CovidGAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 8, 91916\u201391923 (2020)","DOI":"10.1109\/ACCESS.2020.2994762"},{"key":"47_CR26","doi-asserted-by":"crossref","unstructured":"Wang, L., Wong, A.: Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. arXiv preprint arXiv:2003.09871 (2020)","DOI":"10.1038\/s41598-020-76550-z"},{"key":"47_CR27","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: International Conference on Machine Learning, pp. 7354\u20137363. PMLR (2019)"},{"key":"47_CR28","unstructured":"Zhao, J., Zhang, Y., He, X., Xie, P.: Covid-CT-dataset: a CT scan dataset about covid-19. arXiv preprint arXiv:2003.13865 (2020)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86340-1_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T12:17:11Z","timestamp":1631276231000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86340-1_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863395","9783030863401"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86340-1_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","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":"265","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":"4","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":"53% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}