{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:55:26Z","timestamp":1771548926429,"version":"3.50.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723896","type":"print"},{"value":"9783031723902","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\/978-3-031-72390-2_24","type":"book-chapter","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T10:03:14Z","timestamp":1729591394000},"page":"251-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multi-Dataset Multi-Task Learning for\u00a0COVID-19 Prognosis"],"prefix":"10.1007","author":[{"given":"Filippo","family":"Ruffini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"Tronchin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoru","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Soda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valerio","family":"Guarrasi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar, A., et al.: Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput. Biol. Med. 126, 104037 (2020)","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"24_CR2","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.3390\/diagnostics11101812","volume":"11","author":"J Bae","year":"2021","unstructured":"Bae, J., et al.: Predicting mechanical ventilation and mortality in COVID-19 using radiomics and deep learning on chest radiographs: a multi-institutional study. Diagnostics 11(10), 1812 (2021)","journal-title":"Diagnostics"},{"key":"24_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108499","volume":"124","author":"G Bao","year":"2022","unstructured":"Bao, G., et al.: COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. Pattern Recogn. 124, 108499 (2022)","journal-title":"Pattern Recogn."},{"issue":"5","key":"24_CR4","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s11547-020-01200-3","volume":"125","author":"A Borghesi","year":"2020","unstructured":"Borghesi, A., et al.: COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol. Med. (Torino) 125(5), 509\u2013513 (2020)","journal-title":"Radiol. Med. (Torino)"},{"issue":"3","key":"24_CR5","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s11547-022-01456-x","volume":"127","author":"A Borghesi","year":"2022","unstructured":"Borghesi, A., et al.: Chest X-ray versus chest computed tomography for outcome prediction in hospitalized patients with COVID-19. Radiol. Med. (Torino) 127(3), 305\u2013308 (2022)","journal-title":"Radiol. Med. (Torino)"},{"issue":"4","key":"24_CR6","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s10654-023-00973-x","volume":"38","author":"C Buttia","year":"2023","unstructured":"Buttia, C., et al.: Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur. J. Epidemiol. 38(4), 355\u2013372 (2023)","journal-title":"Eur. J. Epidemiol."},{"key":"24_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana, R.: Multitask learning. Machine learning 28, 41\u201375 (1997)","journal-title":"Multitask learning. Machine learning"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Cohen, J.P., et\u00a0al.: Predicting covid-19 pneumonia severity on chest x-ray with deep learning. Cureus 12(7) (2020)","DOI":"10.7759\/cureus.9448"},{"issue":"1","key":"24_CR9","doi-asserted-by":"publisher","first-page":"12791","DOI":"10.1038\/s41598-022-15013-z","volume":"12","author":"VV Danilov","year":"2022","unstructured":"Danilov, V.V., et al.: Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow. Sci. Rep. 12(1), 12791 (2022)","journal-title":"Sci. Rep."},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Guarrasi, V., et\u00a0al.: A multi-expert system to detect COVID-19 cases in X-ray images. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). pp. 395\u2013400. IEEE (2021)","DOI":"10.1109\/CBMS52027.2021.00090"},{"key":"24_CR11","doi-asserted-by":"publisher","first-page":"110825","DOI":"10.1016\/j.patcog.2024.110825","volume":"156","author":"V Guarrasi","year":"2024","unstructured":"Guarrasi, V., et al.: Multimodal explainability via latent shift applied to COVID-19 stratification. Pattern Recogn. 156, 110825 (2024)","journal-title":"Pattern Recogn."},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Guarrasi, V., et\u00a0al.: Optimized fusion of CNNs to diagnose pulmonary diseases on chest X-Rays. In: International Conference on Image Analysis and Processing. pp. 197\u2013209. Springer (2022)","DOI":"10.1007\/978-3-031-06427-2_17"},{"key":"24_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108242","volume":"121","author":"V Guarrasi","year":"2022","unstructured":"Guarrasi, V., et al.: Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays. Pattern Recogn. 121, 108242 (2022)","journal-title":"Pattern Recogn."},{"key":"24_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106625","volume":"154","author":"V Guarrasi","year":"2023","unstructured":"Guarrasi, V., et al.: Multi-objective optimization determines when, which and how to fuse deep networks: An application to predict COVID-19 outcomes. Comput. Biol. Med. 154, 106625 (2023)","journal-title":"Comput. Biol. Med."},{"key":"24_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107828","volume":"113","author":"K He","year":"2021","unstructured":"He, K., et al.: Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images. Pattern Recogn. 113, 107828 (2021)","journal-title":"Pattern Recogn."},{"key":"24_CR16","unstructured":"Hosseini, S., et\u00a0al.: Distill-2MD-MTL: Data distillation based on multi-dataset multi-domain multi-task frame work to solve face related tasksks, multi task learning, semi-supervised learning. arXiv preprint arXiv:1907.03402 (2019)"},{"issue":"5","key":"24_CR17","doi-asserted-by":"publisher","first-page":"e286","DOI":"10.1016\/S2589-7500(21)00039-X","volume":"3","author":"Z Jiao","year":"2021","unstructured":"Jiao, Z., et al.: Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. The Lancet Digital Health 3(5), e286\u2013e294 (2021)","journal-title":"The Lancet Digital Health"},{"key":"24_CR18","unstructured":"Kaiser, L., et\u00a0al.: One model to learn them all. arXiv preprint arXiv:1706.05137 (2017)"},{"key":"24_CR19","unstructured":"Kapidis, G., et\u00a0al.: Multi-dataset, multitask learning of egocentric vision tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Ke, A., et\u00a0al.: CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation. In: Proceedings of the conference on health, inference, and learning. pp. 116\u2013124 (2021)","DOI":"10.1145\/3450439.3451867"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Kulkarni, A.R., et\u00a0al.: Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19. BMJ innovations pp. bmjinnov\u20132020 (2021)","DOI":"10.1136\/bmjinnov-2020-000593"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Lee, C., et\u00a0al.: Deephit: A deep learning approach to survival analysis with competing risks. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11842"},{"issue":"13","key":"24_CR23","doi-asserted-by":"publisher","first-page":"5007","DOI":"10.3390\/s22135007","volume":"22","author":"JH Lee","year":"2022","unstructured":"Lee, J.H., et al.: Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort. Sensors 22(13), 5007 (2022)","journal-title":"Sensors"},{"issue":"4","key":"24_CR24","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.10.4.044504","volume":"10","author":"H Li","year":"2023","unstructured":"Li, H., et al.: Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. Journal of Medical Imaging 10(4), 044504 (2023)","journal-title":"Journal of Medical Imaging"},{"key":"24_CR25","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.982289","volume":"10","author":"Z Li","year":"2022","unstructured":"Li, Z., et al.: A multistage multimodal deep learning model for disease severity assessment and early warnings of high-risk patients of COVID-19. Front. Public Health 10, 982289 (2022)","journal-title":"Front. Public Health"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Rahman, T., et\u00a0al.: BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data. Neural Computing and Applications pp. 1\u201323 (2023)","DOI":"10.1007\/s00521-023-08606-w"},{"issue":"1","key":"24_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-021-02720-w","volume":"19","author":"V Sch\u00f6ning","year":"2021","unstructured":"Sch\u00f6ning, V., et al.: Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital. J. Transl. Med. 19(1), 1\u201311 (2021)","journal-title":"J. Transl. Med."},{"key":"24_CR28","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"14","author":"F Shi","year":"2020","unstructured":"Shi, F., et al.: Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev. Biomed. Eng. 14, 4\u201315 (2020)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"24_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102046","volume":"71","author":"A Signoroni","year":"2021","unstructured":"Signoroni, A., et al.: BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset. Med. Image Anal. 71, 102046 (2021)","journal-title":"Med. Image Anal."},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Soda, P., et\u00a0al.: AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study. Medical image analysis 74, 102216 (2021)","DOI":"10.1016\/j.media.2021.102216"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Wang, S., et\u00a0al.: A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. European Respiratory Journal 56(2) (2020)","DOI":"10.1183\/13993003.00775-2020"},{"issue":"5","key":"24_CR32","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/acfe9c","volume":"20","author":"Y Xie","year":"2023","unstructured":"Xie, Y., et al.: Cross-dataset transfer learning for motor imagery signal classification via multi-task learning and pre-training. J. Neural Eng. 20(5), 056037 (2023)","journal-title":"J. Neural Eng."},{"key":"24_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106496","volume":"153","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y., et al.: Multi-task deep learning for medical image computing and analysis: A review. Comput. Biol. Med. 153, 106496 (2023)","journal-title":"Comput. Biol. Med."},{"issue":"7","key":"24_CR34","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0236621","volume":"15","author":"J Zhu","year":"2020","unstructured":"Zhu, J., et al.: Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PLoS ONE 15(7), e0236621 (2020)","journal-title":"PLoS ONE"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72390-2_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T10:08:01Z","timestamp":1729591681000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72390-2_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723896","9783031723902"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72390-2_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}