{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:02:02Z","timestamp":1774540922318,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031569494","type":"print"},{"value":"9783031569500","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-56950-0_5","type":"book-chapter","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T03:01:57Z","timestamp":1711594917000},"page":"47-59","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving the\u00a0Efficiency of\u00a0Multimodal Approach for\u00a0Chest X-Ray"],"prefix":"10.1007","author":[{"given":"Jiblal","family":"Upadhya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge","family":"Vargas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khem","family":"Poudel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaishree","family":"Ranganathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"5_CR1","unstructured":"Aydin, F., Zhang, M., Ananda-Rajah, M., Haffari, G.: Medical multimodal classifiers under scarce data condition. arXiv preprint arXiv:1902.08888 (2019)"},{"issue":"2","key":"5_CR2","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423\u2013443 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"5_CR3","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1093\/jamia\/ocv080","volume":"23","author":"D Demner-Fushman","year":"2016","unstructured":"Demner-Fushman, D., et al.: Preparing a collection of radiology examinations for distribution and retrieval. J. Am. Med. Inform. Assoc. 23(2), 304\u2013310 (2016)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Hadjiyski, N., Vosoughi, A., Wismueller, A.: Cross modal global local representation learning from radiology reports and x-ray chest images. arXiv preprint arXiv:2301.10951 (2023)","DOI":"10.1117\/12.2654520"},{"key":"5_CR5","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":"5_CR6","unstructured":"Jain, S., et al.: Radgraph: extracting clinical entities and relations from radiology reports. arXiv preprint arXiv:2106.14463 (2021)"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: Mimic-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6(1), 317 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Kline, A., et al.: Multimodal machine learning in precision health: a scoping review. NPJ Digit. Med. 5(1), 171 (2022)","DOI":"10.1038\/s41746-022-00712-8"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Kohankhaki, M., Ayad, A., Barhoush, M., Leibe, B., Schmeink, A.: Radiopaths: deep multimodal analysis on chest radiographs. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 3613\u20133621. IEEE (2022)","DOI":"10.1109\/BigData55660.2022.10020356"},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"19","DOI":"10.3389\/fdata.2020.00019","volume":"3","author":"K Lopez","year":"2020","unstructured":"Lopez, K., Fodeh, S.J., Allam, A., Brandt, C.A., Krauthammer, M.: Reducing annotation burden through multimodal learning. Front. Big Data 3, 19 (2020)","journal-title":"Front. Big Data"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Manocha, A., Bhatia, M.: A novel deep fusion strategy for COVID-19 prediction using multimodality approach. Comput. Electr. Eng. 103, 108, 274 (2022)","DOI":"10.1016\/j.compeleceng.2022.108274"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Meedeniya, D., Kumarasinghe, H., Kolonne, S., Fernando, C., De\u00a0la Torre\u00a0D\u00edez, I., Marques, G.: chest x-ray analysis empowered with deep learning: a systematic review. Appl. Soft Comput. 109319 (2022)","DOI":"10.1016\/j.asoc.2022.109319"},{"issue":"12","key":"5_CR14","doi-asserted-by":"publisher","first-page":"6070","DOI":"10.1109\/JBHI.2022.3207502","volume":"26","author":"JH Moon","year":"2022","unstructured":"Moon, J.H., Lee, H., Shin, W., Kim, Y.H., Choi, E.: Multi-modal understanding and generation for medical images and text via vision-language pre-training. IEEE J. Biomed. Health Inform. 26(12), 6070\u20136080 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"3","key":"5_CR15","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1007\/s42979-022-01653-5","volume":"4","author":"SE Mukhi","year":"2023","unstructured":"Mukhi, S.E., Varshini, R.T., Sherley, S.E.F.: Diagnosis of COVID-19 from multimodal imaging data using optimized deep learning techniques. SN Comput. Sc. 4(3), 212 (2023)","journal-title":"SN Comput. Sc."},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Singh, S., Karimi, S., Ho-Shon, K., Hamey, L.: From chest x-rays to radiology reports: a multimodal machine learning approach. In: 2019 Digital Image Computing: Techniques and Applications (DICTA), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/DICTA47822.2019.8945819"},{"key":"5_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/978-3-030-87240-3_58","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Yu","year":"2021","unstructured":"Yu, Y., Hu, P., Lin, J., Krishnaswamy, P.: Multimodal multitask deep learning for x-ray image retrieval. In: de Bruijne, M., et al. (eds.) MICCAI 2021, Part V. LNCS, vol. 12905, pp. 603\u2013613. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_58"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Second International Conference on Advances in Computing Research (ACR\u201924)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56950-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T03:09:42Z","timestamp":1711595382000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56950-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031569494","9783031569500"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56950-0_5","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Computing Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acr2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iicser.org\/ACR24","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}