{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T12:48:57Z","timestamp":1726231737868},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031445200"},{"type":"electronic","value":"9783031445217"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44521-7_18","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T19:01:58Z","timestamp":1696100518000},"page":"185-194","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-task Learning for\u00a0Hierarchically-Structured Images: Study on\u00a0Echocardiogram View Classification"],"prefix":"10.1007","author":[{"given":"Jerome","family":"Charton","sequence":"first","affiliation":[]},{"given":"Hui","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Sekeun","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Carola Maraboto","family":"Gonzalez","sequence":"additional","affiliation":[]},{"given":"Jay","family":"Khambhati","sequence":"additional","affiliation":[]},{"given":"Justin","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Jeena","family":"DeFrancesco","sequence":"additional","affiliation":[]},{"given":"Anam","family":"Waheed","sequence":"additional","affiliation":[]},{"given":"Sylwia","family":"Marciniak","sequence":"additional","affiliation":[]},{"given":"Filipe","family":"Moura","sequence":"additional","affiliation":[]},{"given":"Rhanderson","family":"Cardoso","sequence":"additional","affiliation":[]},{"given":"Bruno","family":"Lima","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Picard","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Quanzheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1016\/j.procs.2015.02.084","volume":"46","author":"G Balaji","year":"2015","unstructured":"Balaji, G., Subashini, T., Chidambaram, N.: Automatic classification of cardiac views in echocardiogram using histogram and statistical features. Procedia Comput. Sci. 46, 1569\u20131576 (2015)","journal-title":"Procedia Comput. Sci."},{"key":"18_CR2","unstructured":"Bannur, S., et al.: Hierarchical analysis of visual COVID-19 features from chest radiographs. ArXiv abs\/2107.06618 (2021)"},{"key":"18_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-031-16902-1_7","volume-title":"Simplifying Medical Ultrasound","author":"J Charton","year":"2022","unstructured":"Charton, J., et al.: View classification of color doppler echocardiography via automatic alignment between doppler and b-mode imaging. In: Aylward, S., Noble, J.A., Hu, Y., Lee, S.L., Baum, Z., Min, Z. (eds.) ASMUS 2022. LNCS, pp. 64\u201371. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16902-1_7"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., Miao, S., Xu, D., Hager, G.D., Harrison, A.P.: Deep hiearchical multi-label classification applied to chest X-ray abnormality taxonomies. CoRR abs\/2009.05609 (2020). https:\/\/arxiv.org\/abs\/2009.05609","DOI":"10.1016\/j.media.2020.101811"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Hsu, B.W.Y., Tseng, V.S.: Hierarchy-aware contrastive learning with late fusion for skin lesion classification. Comput. Methods Programs Biomed. 216, 106666 (2022)","DOI":"10.1016\/j.cmpb.2022.106666"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Khaleel, M., Tavanapong, W., Wong, J., Oh, J., De Groen, P.: Hierarchical visual concept interpretation for medical image classification. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 25\u201330. IEEE (2021)","DOI":"10.1109\/CBMS52027.2021.00012"},{"key":"18_CR7","doi-asserted-by":"publisher","unstructured":"Khaleel, M., Tavanapong, W., Wong, J., Oh, J., de Groen, P.: Hierarchical visual concept interpretation for medical image classification. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 25\u201330 (2021). https:\/\/doi.org\/10.1109\/CBMS52027.2021.00012","DOI":"10.1109\/CBMS52027.2021.00012"},{"issue":"1","key":"18_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s12574-020-00496-4","volume":"19","author":"K Kusunose","year":"2021","unstructured":"Kusunose, K.: Steps to use artificial intelligence in echocardiography. J. Echocardiogr. 19(1), 21\u201327 (2021)","journal-title":"J. Echocardiogr."},{"key":"18_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/978-3-030-32245-8_76","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Liao","year":"2019","unstructured":"Liao, Z., et al.: Echocardiography view classification using quality transfer star generative adversarial networks. In: Shen, D., et al. (eds.) MICCAI 2019, Part II. LNCS, vol. 11765, pp. 687\u2013695. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_76"},{"issue":"1","key":"18_CR10","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s41746-017-0013-1","volume":"1","author":"A Madani","year":"2018","unstructured":"Madani, A., Arnaout, R., Mofrad, M., Arnaout, R.: Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit. Med. 1(1), 6 (2018)","journal-title":"NPJ Digit. Med."},{"issue":"1","key":"18_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.echo.2018.06.004","volume":"32","author":"C Mitchell","year":"2019","unstructured":"Mitchell, C., et al.: Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American society of echocardiography. J. Am. Soc. Echocardiogr. 32(1), 1\u201364 (2019)","journal-title":"J. Am. Soc. Echocardiogr."},{"issue":"2","key":"18_CR12","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.ultrasmedbio.2018.07.024","volume":"45","author":"A \u00d8stvik","year":"2019","unstructured":"\u00d8stvik, A., Smistad, E., Aase, S.A., Haugen, B.O., Lovstakken, L.: Real-time standard view classification in transthoracic echocardiography using convolutional neural networks. Ultras. Med. Biol. 45(2), 374\u2013384 (2019)","journal-title":"Ultras. Med. Biol."},{"key":"18_CR13","unstructured":"Otto, C.M.: Textbook of Clinical Echocardiography. Elsevier Health Sciences (2013)"},{"key":"18_CR14","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.neucom.2020.03.127","volume":"437","author":"HH Pham","year":"2021","unstructured":"Pham, H.H., Le, T.T., Tran, D.Q., Ngo, D.T., Nguyen, H.Q.: Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels. Neurocomputing 437, 186\u2013194 (2021)","journal-title":"Neurocomputing"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Sanh, V., Wolf, T., Ruder, S.: A hierarchical multi-task approach for learning embeddings from semantic tasks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 6949\u20136956 (2019)","DOI":"10.1609\/aaai.v33i01.33016949"},{"key":"18_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11886-020-01329-7","volume":"22","author":"K Seetharam","year":"2020","unstructured":"Seetharam, K., Raina, S., Sengupta, P.P.: The role of artificial intelligence in echocardiography. Curr. Cardiol. Rep. 22, 1\u20138 (2020)","journal-title":"Curr. Cardiol. Rep."},{"key":"18_CR17","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221\u2013248 (2017)","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"18_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Vaseli, H., et al.: Designing lightweight deep learning models for echocardiography view classification. In: Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10951, pp. 93\u201399. SPIE (2019)","DOI":"10.1117\/12.2512913"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Wu, H., Bowers, D.M., Huynh, T.T., Souvenir, R.: Echocardiogram view classification using low-level features. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 752\u2013755. IEEE (2013)","DOI":"10.1109\/ISBI.2013.6556584"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: HKGB: an inclusive, extensible, intelligent, semi-auto-constructed knowledge graph framework for healthcare with clinicians\u2019 expertise incorporated. Inf. Process. Manag. 57(6), 102324 (2020)","DOI":"10.1016\/j.ipm.2020.102324"},{"key":"18_CR22","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neucom.2018.02.109","volume":"395","author":"J Zhao","year":"2020","unstructured":"Zhao, J., Peng, Y., He, X.: Attribute hierarchy based multi-task learning for fine-grained image classification. Neurocomputing 395, 150\u2013159 (2020)","journal-title":"Neurocomputing"},{"key":"18_CR23","doi-asserted-by":"publisher","unstructured":"Zhu, X., Bain, M.: B-CNN: branch convolutional neural network for hierarchical classification. ArXiv (2017). https:\/\/doi.org\/10.48550\/ARXIV.1709.09890","DOI":"10.48550\/ARXIV.1709.09890"}],"container-title":["Lecture Notes in Computer Science","Simplifying Medical Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44521-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T19:03:48Z","timestamp":1696100628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44521-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031445200","9783031445217"],"references-count":23,"URL":"http:\/\/dx.doi.org\/10.1007\/978-3-031-44521-7_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ASMUS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Advances in Simplifying Medical Ultrasound","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"asmus2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-ultrasound.github.io\/#\/asmus23","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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","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":"19","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":"63% - 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":"3","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)"}}]}}