{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T14:04:34Z","timestamp":1772460274426,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049704","type":"print"},{"value":"9783032049711","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04971-1_17","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T17:10:13Z","timestamp":1758301813000},"page":"175-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EchoingECG: An Electrocardiogram Cross-Modal Model for\u00a0Echocardiogram Tasks"],"prefix":"10.1007","author":[{"given":"Yuan","family":"Gao","sequence":"first","affiliation":[]},{"given":"Sangwook","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Chris","family":"McIntosh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","volume":"76","author":"M Abdar","year":"2021","unstructured":"Abdar, M., et al.: A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf. Fusion 76, 243\u2013297 (2021)","journal-title":"Inf. Fusion"},{"issue":"5","key":"17_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0302639","volume":"19","author":"SM Al Younis","year":"2024","unstructured":"Al Younis, S.M., et al.: Prediction of hf patients with distinct left ventricular ejection fraction levels using circadian ecg features and machine learning. PLoS ONE 19(5), e0302639 (2024)","journal-title":"PLoS ONE"},{"issue":"1","key":"17_CR3","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41591-018-0240-2","volume":"25","author":"ZI Attia","year":"2019","unstructured":"Attia, Z.I., Kapa, S., et al.: Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat. Med. 25(1), 70\u201374 (2019)","journal-title":"Nat. Med."},{"key":"17_CR4","unstructured":"Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 359\u2013370 (1994)"},{"issue":"22","key":"17_CR5","doi-asserted-by":"publisher","first-page":"2002","DOI":"10.1093\/eurheartj\/ehad782","volume":"45","author":"S Bhave","year":"2024","unstructured":"Bhave, S., et al.: Deep learning to detect left ventricular structural abnormalities in chest x-rays. Eur. Heart J. 45(22), 2002\u20132012 (2024)","journal-title":"Eur. Heart J."},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Christensen, M., Vukadinovic, M., Yuan, N., Ouyang, D.: Vision\u2013language foundation model for echocardiogram interpretation. Nat. Med.\u00a01\u20138 (2024)","DOI":"10.1038\/s41591-024-02959-y"},{"key":"17_CR7","unstructured":"Chun, S.: Improved probabilistic image-text representations. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Chun, S., Oh, S.J., De\u00a0Rezende, R.S., Kalantidis, Y., Larlus, D.: Probabilistic embeddings for cross-modal retrieval. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8415\u20138424 (2021)","DOI":"10.1109\/CVPR46437.2021.00831"},{"key":"17_CR9","unstructured":"Dubey, A., et\u00a0al.: The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Gao, Y., Kim, S., Austin, D.E., McIntosh, C.: Medbind: unifying language and multimodal medical data embeddings. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 218\u2013228. Springer, Heidelberg (2024)","DOI":"10.1007\/978-3-031-72390-2_21"},{"issue":"3","key":"17_CR11","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1002\/joa3.12062","volume":"34","author":"KA Gatzoulis","year":"2018","unstructured":"Gatzoulis, K.A., et al.: Signal-averaged electrocardiography: past, present, and future. J. Arrhythmia 34(3), 222\u2013229 (2018)","journal-title":"J. Arrhythmia"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Girdhar, R., et al.: Imagebind: one embedding space to bind them all. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.01457"},{"key":"17_CR13","unstructured":"Goel, A., et\u00a0al.: Llms accelerate annotation for medical information extraction. In: Machine Learning for Health (ML4H), pp. 82\u2013100. PMLR (2023)"},{"key":"17_CR14","unstructured":"Gow, B., et al.: Mimic-iv-echo: echocardiogram matched subset. Physionet (2024)"},{"key":"17_CR15","unstructured":"Gow, B., et\u00a0al.: Mimic-iv-ecg-diagnostic electrocardiogram matched subset. Physionet (2023)"},{"issue":"14","key":"17_CR16","doi-asserted-by":"publisher","first-page":"3072","DOI":"10.3390\/s19143072","volume":"19","author":"R Jaros","year":"2019","unstructured":"Jaros, R., Martinek, R., Danys, L.: Comparison of different electrocardiography with vectorcardiography transformations. Sensors 19(14), 3072 (2019)","journal-title":"Sensors"},{"issue":"1","key":"17_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AE Johnson","year":"2023","unstructured":"Johnson, A.E., et al.: Mimic-iv, a freely accessible electronic health record dataset. Sci. Data 10(1), 1 (2023)","journal-title":"Sci. Data"},{"key":"17_CR18","unstructured":"Kenton, J.D.M.W.C., Toutanova, L.K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, vol.\u00a01, p.\u00a02 (2019)"},{"issue":"4","key":"17_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0284791","volume":"18","author":"F Khan","year":"2023","unstructured":"Khan, F., Yu, X., Yuan, Z., Rehman, A.U.: Ecg classification using 1-d convolutional deep residual neural network. PLoS ONE 18(4), e0284791 (2023)","journal-title":"PLoS ONE"},{"issue":"101","key":"17_CR20","first-page":"1","volume":"3","author":"R Kher","year":"2019","unstructured":"Kher, R., et al.: Signal processing techniques for removing noise from ecg signals. J. Biomed. Eng. Res 3(101), 1\u20139 (2019)","journal-title":"J. Biomed. Eng. Res"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Kusunose, K.: Transforming echocardiography: the role of artificial intelligence in enhancing diagnostic accuracy and accessibility. Internal Med. (2025)","DOI":"10.2169\/internalmedicine.4171-24"},{"issue":"4","key":"17_CR22","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"17_CR23","unstructured":"Li, J., Liu, C., Cheng, S., Arcucci, R., Hong, S.: Frozen language model helps ecg zero-shot learning. In: Medical Imaging with Deep Learning. PMLR (2024)"},{"key":"17_CR24","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2018)"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Mart\u0131n-Yebra, A., et al.: The music database: sudden cardiac death in heart failure patients. Physionet (2024)","DOI":"10.22489\/CinC.2024.355"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"McKeen, K., Oliva, L., Masood, S., Toma, A., Rubin, B., Wang, B.: Ecg-fm: an open electrocardiogram foundation model. arXiv preprint arXiv:2408.05178 (2024)","DOI":"10.1093\/jamiaopen\/ooaf122"},{"key":"17_CR27","unstructured":"Mehari, T., Strodthoff, N.: Advancing the state-of-the-art for ecg analysis through structured state space models. arXiv preprint arXiv:2211.07579 (2022)"},{"key":"17_CR28","unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"17_CR29","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"17_CR30","unstructured":"Raghu, A., Shanmugam, D., Pomerantsev, E., Guttag, J., Stultz, C.M.: Data augmentation for electrocardiograms. In: Conference on Health, Inference, and Learning, pp. 282\u2013310. PMLR (2022)"},{"issue":"9","key":"17_CR31","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1161\/CIRCULATIONAHA.122.062646","volume":"148","author":"V Sangha","year":"2023","unstructured":"Sangha, V., et al.: Detection of lvef from electrocardiographic images. Circulation 148(9), 765\u2013777 (2023)","journal-title":"Circulation"},{"issue":"5","key":"17_CR32","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1109\/JBHI.2020.3022989","volume":"25","author":"N Strodthoff","year":"2020","unstructured":"Strodthoff, N., Wagner, P., Schaeffter, T., Samek, W.: Deep learning for ecg analysis: benchmarks and insights from ptb-xl. IEEE J. Biomed. Health Inf. 25(5), 1519\u20131528 (2020)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Z., Agarwal, D., Sun, J.: Medclip: contrastive learning from unpaired medical images and text. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 3876\u20133887 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"key":"17_CR34","doi-asserted-by":"crossref","unstructured":"Xue, L., et al.: Ulip: learning a unified representation of language, images, and point clouds for 3d understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1179\u20131189 (2023)","DOI":"10.1109\/CVPR52729.2023.00120"},{"issue":"5","key":"17_CR35","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1038\/s41591-021-01335-4","volume":"27","author":"X Yao","year":"2021","unstructured":"Yao, X., Rushlow, D.R., Inselman, J.W., McCoy, R.G., et al.: Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat. Med. 27(5), 815\u2013819 (2021)","journal-title":"Nat. Med."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04971-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T13:10:35Z","timestamp":1772457035000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04971-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049704","9783032049711"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04971-1_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}