{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:08:09Z","timestamp":1771952889364,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723773","type":"print"},{"value":"9783031723780","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\/978-3-031-72378-0_56","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T07:02:53Z","timestamp":1727852573000},"page":"598-607","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Symptom Disentanglement in\u00a0Chest X-Ray Images for\u00a0Fine-Grained Progression Learning"],"prefix":"10.1007","author":[{"given":"Ye","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pong C.","family":"Yuen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"issue":"1","key":"56_CR1","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3390\/diagnostics13010159","volume":"13","author":"A Ait Nasser","year":"2023","unstructured":"Ait Nasser, A., Akhloufi, M.A.: A review of recent advances in deep learning models for chest disease detection using radiography. Diagnostics 13(1), 159 (2023)","journal-title":"Diagnostics"},{"key":"56_CR2","doi-asserted-by":"crossref","unstructured":"Bannur, S., et\u00a0al.: Learning to exploit temporal structure for biomedical vision-language processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15016\u201315027 (2023)","DOI":"10.1109\/CVPR52729.2023.01442"},{"key":"56_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102125","volume":"72","author":"E \u00c7all\u0131","year":"2021","unstructured":"\u00c7all\u0131, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K.G., Murphy, K.: Deep learning for chest x-ray analysis: a survey. Med. Image Anal. 72, 102125 (2021)","journal-title":"Med. Image Anal."},{"key":"56_CR4","doi-asserted-by":"crossref","unstructured":"Dalla\u00a0Serra, F., Wang, C., Deligianni, F., Dalton, J., O\u2019Neil, A.Q.: Controllable chest X-ray report generation from longitudinal representations. In: The 2023 Conference on Empirical Methods in Natural Language Processing (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.325"},{"key":"56_CR5","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/978-3-031-16434-7_60","volume-title":"MICCAI 2022","author":"T Emre","year":"2022","unstructured":"Emre, T., Chakravarty, A., Rivail, A., Riedl, S., Schmidt-Erfurth, U., Bogunovi\u0107, H.: TINC: temporally informed non-contrastive learning for disease progression modeling in retinal OCT volumes. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13432, pp. 625\u2013634. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_60"},{"key":"56_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/978-3-030-87234-2_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"B Hou","year":"2021","unstructured":"Hou, B., Kaissis, G., Summers, R.M., Kainz, B.: RATCHET: medical transformer for\u00a0chest X-ray diagnosis and reporting. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 293\u2013303. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_28"},{"key":"56_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102343","volume":"77","author":"C Huang","year":"2022","unstructured":"Huang, C., et al.: DADP: dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the osteoarthritis initiative. Med. Image Anal. 77, 102343 (2022)","journal-title":"Med. Image Anal."},{"key":"56_CR8","doi-asserted-by":"crossref","unstructured":"Irvin, J., et\u00a0al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Thirty-Third AAAI Conference on Artificial Intelligence (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"56_CR9","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. arXiv preprint arXiv:1901.07042 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"56_CR10","series-title":"LNCS","first-page":"581","volume-title":"MICCAI 2022","author":"G Karwande","year":"2022","unstructured":"Karwande, G., Mbakwe, A.B., Wu, J.T., Celi, L.A., Moradi, M., Lourentzou, I.: CheXRelNet: an anatomy-aware model for tracking longitudinal relationships between chest X-rays. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13431, pp. 581\u2013591. Springer, Cham (2022)"},{"issue":"1","key":"56_CR11","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1038\/s41746-020-0255-1","volume":"3","author":"MD Li","year":"2020","unstructured":"Li, M.D., et al.: Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging. NPJ Digit. Med. 3(1), 48 (2020)","journal-title":"NPJ Digit. Med."},{"key":"56_CR12","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/978-3-031-43907-0_30","volume-title":"MICCAI 2023","author":"W Liang","year":"2023","unstructured":"Liang, W., et al.: Modeling Alzheimers\u2019 disease progression from multi-task and self-supervised learning perspective with brain networks. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14220, pp. 310\u2013319. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_30"},{"key":"56_CR13","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"56_CR14","doi-asserted-by":"crossref","unstructured":"Luo, Y., Shi, M., Tian, Y., Elze, T., Wang, M.: Harvard glaucoma detection and progression: a multimodal multitask dataset and generalization-reinforced semi-supervised learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 20471\u201320482 (2023)","DOI":"10.1109\/ICCV51070.2023.01872"},{"key":"56_CR15","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/978-3-031-43904-9_66","volume-title":"MICCAI 2023","author":"AB Mbakwe","year":"2023","unstructured":"Mbakwe, A.B., Wang, L., Moradi, M., Lourentzou, I.: Hierarchical vision transformers for disease progression detection in chest X-ray images. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14224, pp. 685\u2013695. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43904-9_66"},{"key":"56_CR16","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-031-43907-0_6","volume-title":"MICCAI 2023","author":"P M\u00fcller","year":"2023","unstructured":"M\u00fcller, P., Meissen, F., Brandt, J., Kaissis, G., Rueckert, D.: Anatomy-driven pathology detection on chest X-rays. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14220, pp. 57\u201366. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_6"},{"key":"56_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1007\/978-3-030-32226-7_83","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"DY Oh","year":"2019","unstructured":"Oh, D.Y., Kim, J., Lee, K.J.: Longitudinal change detection on chest X-rays using geometric correlation maps. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 748\u2013756. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_83"},{"issue":"1","key":"56_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12890-020-01286-5","volume":"20","author":"LA Rousan","year":"2020","unstructured":"Rousan, L.A., Elobeid, E., Karrar, M., Khader, Y.: Chest X-ray findings and temporal lung changes in patients with Covid-19 pneumonia. BMC Pulm. Med. 20(1), 1\u20139 (2020)","journal-title":"BMC Pulm. Med."},{"key":"56_CR19","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":"56_CR20","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"56_CR21","doi-asserted-by":"crossref","unstructured":"Wu, B., Ren, S., Li, J., Sun, X., Li, S.M., Wang, Y.: Forecasting irreversible disease via progression learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8117\u20138125 (2021)","DOI":"10.1109\/CVPR46437.2021.00802"},{"key":"56_CR22","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-031-43993-3_11","volume-title":"MICCAI 2023","author":"L Zhang","year":"2023","unstructured":"Zhang, L., et al.: Brain anatomy-guided MRI analysis for assessing clinical progression of cognitive impairment with structural MRI. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14227, pp. 109\u2013119. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43993-3_11"}],"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\/978-3-031-72378-0_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T07:30:24Z","timestamp":1771054224000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72378-0_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723773","9783031723780"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72378-0_56","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":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors\u00a0have 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"}}]}}