{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:40:15Z","timestamp":1765546815187,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030877217"},{"type":"electronic","value":"9783030877224"}],"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\/978-3-030-87722-4_15","type":"book-chapter","created":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T01:17:40Z","timestamp":1632446260000},"page":"158-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9676-6817","authenticated-orcid":false,"given":"Shujaat","family":"Khan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2126-0763","authenticated-orcid":false,"given":"Jaeyoung","family":"Huh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9763-9609","authenticated-orcid":false,"given":"Jong Chul","family":"Ye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"15_CR1","first-page":"265","volume":"16","author":"M Abadi","year":"2016","unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. OSDI 16, 265\u2013283 (2016)","journal-title":"OSDI"},{"issue":"2","key":"15_CR2","first-page":"124","volume":"1","author":"M Blaivas","year":"2020","unstructured":"Blaivas, M., Arntfield, R., White, M.: DIY AI, deep learning network development for automated image classification in a point-of-care ultrasound quality assurance program. J. Am. Coll. Emergency Phys. Open 1(2), 124\u2013131 (2020)","journal-title":"J. Am. Coll. Emergency Phys. Open"},{"issue":"6","key":"15_CR3","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1002\/jum.15206","volume":"39","author":"M Blaivas","year":"2020","unstructured":"Blaivas, M., Blaivas, L.: Are all deep learning architectures alike for point-of-care ultrasound?: evidence from a cardiac image classification model suggests otherwise. J. Ultrasound Med. 39(6), 1187\u20131194 (2020)","journal-title":"J. Ultrasound Med."},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Cristiana, B., et al.: Automated lung ultrasound B-line assessment using a deep learning algorithm. IEEE Trans. Ultrasonics Ferroelectrics Frequency Control 67, 2312 (2020)","DOI":"10.1109\/TUFFC.2020.3002249"},{"key":"15_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-030-59520-3_13","volume-title":"Simulation and Synthesis in Medical Imaging","author":"M Escobar","year":"2020","unstructured":"Escobar, M., Castillo, A., Romero, A., Arbel\u00e1ez, P.: UltraGAN: ultrasound enhancement through adversarial generation. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds.) SASHIMI 2020. LNCS, vol. 12417, pp. 120\u2013130. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59520-3_13"},{"issue":"2","key":"15_CR6","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.amjmed.2018.10.012","volume":"132","author":"PJ Han","year":"2019","unstructured":"Han, P.J., Tsai, B.T., Martin, J.W., Keen, W.D., Waalen, J., Kimura, B.J.: Evidence basis for a point-of-care ultrasound examination to refine referral for outpatient echocardiography. Am. J. Med. 132(2), 227\u2013233 (2019)","journal-title":"Am. J. Med."},{"issue":"6","key":"15_CR7","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1109\/TMI.2020.2970867","volume":"39","author":"O Huang","year":"2020","unstructured":"Huang, O., et al.: Mimicknet, mimicking clinical image post-processing under black-box constraints. IEEE Trans. Med. Imag. 39(6), 2277\u20132286 (2020)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"5","key":"15_CR8","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TUFFC.2019.2903795","volume":"66","author":"D Hyun","year":"2019","unstructured":"Hyun, D., Brickson, L.L., Looby, K.T., Dahl, J.J.: Beamforming and speckle reduction using neural networks. IEEE Trans. Ultrasonics Ferroelectrics Frequency Control 66(5), 898\u2013910 (2019)","journal-title":"IEEE Trans. Ultrasonics Ferroelectrics Frequency Control"},{"issue":"5","key":"15_CR9","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1007\/s11548-020-02141-y","volume":"15","author":"MH Jafari","year":"2020","unstructured":"Jafari, M.H., et al.: Cardiac point-of-care to cart-based ultrasound translation using constrained CycleGAN. Int. J. Comput. Assist. Radiol. Surg. 15(5), 877\u2013886 (2020)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"15_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1007\/978-3-030-32254-0_69","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Khan","year":"2019","unstructured":"Khan, S., Huh, J., Ye, J.C.: Deep learning-based universal beamformer for ultrasound imaging. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 619\u2013627. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_69"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Khan, S., Huh, J., Ye, J.C.: Universal plane-wave compounding for high quality us imaging using deep learning. In: 2019 IEEE International Ultrasonics Symposium (IUS), pp. 2345\u20132347. IEEE (2019)","DOI":"10.1109\/ULTSYM.2019.8925679"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Khan, S., Huh, J., Ye, J.C.: Adaptive and compressive beamforming using deep learning for medical ultrasound. IEEE Trans. Ultrasonics Ferroelectrics Frequency Control 67, 1558 (2020)","DOI":"10.1109\/TUFFC.2020.2977202"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Khan, S., Huh, J., Ye, J.C.: Unsupervised deconvolution neural network for high quality ultrasound imaging. In: 2020 IEEE International Ultrasonics Symposium (IUS), pp. 1\u20134. IEEE (2020)","DOI":"10.1109\/IUS46767.2020.9251418"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Khan, S., Huh, J., Ye, J.C.: Switchable deep beamformer for ultrasound imaging using Adain. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 677\u2013680. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433757"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Khan, S., Huh, J., Ye, J.C.: Unsupervised deep learning for accelerated high quality echocardiography. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1738\u20131741. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433770"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Khan, S., Huh, J., Ye, J.C.: Variational formulation of unsupervised deep learning for ultrasound image artifact removal. IEEE Trans. Ultrasonics Ferroelectrics Frequency Control 68, 2086 (2021)","DOI":"10.1109\/TUFFC.2021.3056197"},{"key":"15_CR17","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"11","key":"15_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11886-020-01394-y","volume":"22","author":"L Lee","year":"2020","unstructured":"Lee, L., DeCara, J.M.: Point-of-care ultrasound. Curr. Cardiol. Rep. 22(11), 1\u201310 (2020)","journal-title":"Curr. Cardiol. Rep."},{"key":"15_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-030-33843-5_16","volume-title":"Machine Learning for Medical Image Reconstruction","author":"S Lim","year":"2019","unstructured":"Lim, S., Ye, J.C.: Blind deconvolution microscopy using cycle consistent CNN with explicit PSF layer. In: Knoll, F., Maier, A., Rueckert, D., Ye, J.C. (eds.) MLMIR 2019. LNCS, vol. 11905, pp. 173\u2013180. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33843-5_16"},{"issue":"9","key":"15_CR20","doi-asserted-by":"publisher","first-page":"2010","DOI":"10.1109\/TMI.2018.2809641","volume":"37","author":"AC Luchies","year":"2018","unstructured":"Luchies, A.C., Byram, B.C.: Deep neural networks for ultrasound beamforming. IEEE Trans. Med. Imag. 37(9), 2010\u20132021 (2018)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"4","key":"15_CR21","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1097\/ALN.0000000000003113","volume":"132","author":"D Ramsingh","year":"2020","unstructured":"Ramsingh, D., Bronshteyn, Y.S., Haskins, S., Zimmerman, J.: Perioperative point-of-care ultrasound: from concept to application. Anesthesiology 132(4), 908\u2013916 (2020)","journal-title":"Anesthesiology"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Rodriguez-Molares, A., Rindal, O.M.H., D\u2019hooge, J., M\u00e5s\u00f8y, S.E., Austeng, A., Torp, H.: The generalized contrast-to-noise ratio. In: 2018 IEEE International Ultrasonics Symposium (IUS), pp. 1\u20134. IEEE (2018)","DOI":"10.1109\/ULTSYM.2018.8580101"},{"issue":"8","key":"15_CR23","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TMI.2020.2994459","volume":"39","author":"S Roy","year":"2020","unstructured":"Roy, S., et al.: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imag. 39(8), 2676\u20132687 (2020)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"7","key":"15_CR24","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1002\/jum.14860","volume":"38","author":"H Shokoohi","year":"2019","unstructured":"Shokoohi, H., LeSaux, M.A., Roohani, Y.H., Liteplo, A., Huang, C., Blaivas, M.: Enhanced point-of-care ultrasound applications by integrating automated feature-learning systems using deep learning. J. Ultrasound Med. 38(7), 1887\u20131897 (2019)","journal-title":"J. Ultrasound Med."},{"issue":"4","key":"15_CR25","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1109\/TMI.2019.2941271","volume":"39","author":"O Solomon","year":"2019","unstructured":"Solomon, O., et al.: Deep unfolded robust PCA with application to clutter suppression in ultrasound. IEEE Trans. Med. Imag. 39(4), 1051\u20131063 (2019)","journal-title":"IEEE Trans. Med. Imag."},{"key":"15_CR26","unstructured":"Toth, J.: Utility of Point-of-Care Ultrasound Across Clinical Applications Spurs Continued Growth (2021). https:\/\/www.itnonline.com\/article\/utility-point-care-ultrasound-across-clinical-applications-spurs-continued-growth-0"},{"key":"15_CR27","doi-asserted-by":"publisher","first-page":"m1328","DOI":"10.1136\/bmj.m1328","volume":"369","author":"L Wynants","year":"2020","unstructured":"Wynants, L., et al.: Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. BMJ 369, m1328 (2020). https:\/\/doi.org\/10.1136\/bmj.m1328","journal-title":"BMJ"},{"issue":"2","key":"15_CR28","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/TMI.2018.2864821","volume":"38","author":"YH Yoon","year":"2018","unstructured":"Yoon, Y.H., Khan, S., Huh, J., Ye, J.C.: Efficient b-mode ultrasound image reconstruction from sub-sampled RF data using deep learning. IEEE Trans. Med. Imag. 38(2), 325\u2013336 (2018)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"1","key":"15_CR29","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1109\/TBME.2019.2912986","volume":"67","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Wang, Y., Guo, Y., Qi, Y., Yu, J.: Image quality improvement of hand-held ultrasound devices with a two-stage generative adversarial network. IEEE Trans. Biomed. Eng. 67(1), 298\u2013311 (2019)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Zhu, L., Fu, C.W., Brown, M.S., Heng, P.A.: A non-local low-rank framework for ultrasound speckle reduction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5650\u20135658 (2017)","DOI":"10.1109\/CVPR.2017.60"}],"container-title":["Lecture Notes in Computer Science","Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87722-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T08:21:55Z","timestamp":1680769315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87722-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030877217","9783030877224"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87722-4_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FAIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Affordable Healthcare and AI for Resource Diverse Global Health","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fair22021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/fair-workshop-2021\/home","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","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":"10","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":"59% - 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":"2.8","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)"}},{"value":"The conference was held virtually 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)"}}]}}