{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:47:22Z","timestamp":1792025242272,"version":"4.4.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032353863","type":"print"},{"value":"9783032353870","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-35387-0_9","type":"book-chapter","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T09:07:09Z","timestamp":1786439229000},"page":"122-135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DINO-Med: A Unified Patch-Based Adaptation Framework for\u00a0Multi-Modal Medical Image Analysis Applied to\u00a0Liver Fibrosis Staging"],"prefix":"10.1007","author":[{"given":"Boya","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizhe","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"issue":"1","key":"9_CR1","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jhep.2013.09.002","volume":"60","author":"R Banerjee","year":"2014","unstructured":"Banerjee, R., et al.: Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease. J. Hepatol. 60(1), 69\u201377 (2014)","journal-title":"J. Hepatol."},{"issue":"6","key":"9_CR2","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1053\/jhep.2003.09022","volume":"38","author":"P Bedossa","year":"2003","unstructured":"Bedossa, P., Darg\u00e8re, D., Paradis, V.: Sampling variability of liver fibrosis in chronic hepatitis c. Hepatology 38(6), 1449\u20131457 (2003)","journal-title":"Hepatology"},{"issue":"7","key":"9_CR3","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1056\/NEJM200102153440706","volume":"344","author":"AA Bravo","year":"2001","unstructured":"Bravo, A.A., Sheth, S.G., Chopra, S.: Liver biopsy. N. Engl. J. Med. 344(7), 495\u2013500 (2001)","journal-title":"N. Engl. J. Med."},{"key":"9_CR4","unstructured":"CARE Challenge Organizers: CARE 2025 Track 4: Liver Fibrosis Quantification and Analysis (2025). https:\/\/zmic.org.cn\/care_2025\/track4\/. Accessed 28 May 2026"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"9_CR6","unstructured":"Cheng, J., et\u00a0al.: Sam-med2d. arXiv preprint arXiv:2308.16184 (2023)"},{"issue":"8","key":"9_CR7","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.3390\/diagnostics11081384","volume":"11","author":"Y Dai","year":"2021","unstructured":"Dai, Y., Gao, Y., Liu, F.: Transmed: transformers advance multi-modal medical image classification. Diagnostics 11(8), 1384 (2021)","journal-title":"Diagnostics"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Gao, Z., Liu, Y., Wu, F., Shi, N., Shi, Y., Zhuang, X.: A reliable and interpretable framework of multi-view learning for liver fibrosis staging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 178\u2013188 (2023)","DOI":"10.1007\/978-3-031-43904-9_18"},{"issue":"2","key":"9_CR9","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 278(2), 563\u2013577 (2016)","journal-title":"Radiology"},{"key":"9_CR10","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"},{"issue":"2","key":"9_CR11","doi-asserted-by":"publisher","first-page":"2588","DOI":"10.1109\/TNNLS.2022.3190452","volume":"35","author":"Y He","year":"2022","unstructured":"He, Y., et al.: Learning better registration to learn better few-shot medical image segmentation: authenticity, diversity, and robustness. IEEE Trans. Neural Netw. Learn. Syst. 35(2), 2588\u20132601 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/RBME.2024.3496744","volume":"18","author":"Y He","year":"2024","unstructured":"He, Y., et al.: Foundation model for advancing healthcare: challenges, opportunities and future directions. IEEE Rev. Biomed. Eng. 18, 172\u2013191 (2024)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"9_CR13","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)"},{"key":"9_CR14","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012)"},{"key":"9_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103507","volume":"102","author":"Y Liu","year":"2025","unstructured":"Liu, Y., et al.: Merit: multi-view evidential learning for reliable and interpretable liver fibrosis staging. Med. Image Anal. 102, 103507 (2025)","journal-title":"Med. Image Anal."},{"issue":"7","key":"9_CR16","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1136\/gutjnl-2018-317593","volume":"69","author":"R Loomba","year":"2020","unstructured":"Loomba, R., Adams, L.A.: Advances in non-invasive assessment of hepatic fibrosis. Gut 69(7), 1343\u20131352 (2020)","journal-title":"Gut"},{"key":"9_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106791","volume":"157","author":"ON Manzari","year":"2023","unstructured":"Manzari, O.N., Ahmadabadi, H., Kashiani, H., Shokouhi, S.B., Ayatollahi, A.: Medvit: a robust vision transformer for generalized medical image classification. Comput. Biol. Med. 157, 106791 (2023)","journal-title":"Comput. Biol. Med."},{"key":"9_CR18","unstructured":"Oquab, M., et al.: Dinov2: learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"issue":"2","key":"9_CR19","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1148\/radiol.2018181197","volume":"290","author":"HJ Park","year":"2019","unstructured":"Park, H.J., et al.: Radiomics analysis of gadoxetic acid-enhanced mri for staging liver fibrosis. Radiology 290(2), 380\u2013387 (2019)","journal-title":"Radiology"},{"key":"9_CR20","unstructured":"Sim\u00e9oni, O., et\u00a0al.: Dinov3. arXiv preprint arXiv:2508.10104 (2025)"},{"key":"9_CR21","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"9_CR22","unstructured":"Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: generalizing residual architectures. arXiv preprint arXiv:1603.08029 (2016)"},{"issue":"21","key":"9_CR23","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJ Van Griethuysen","year":"2017","unstructured":"Van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Can. Res. 77(21), e104\u2013e107 (2017)","journal-title":"Can. Res."},{"key":"9_CR24","unstructured":"Wang, B., Li, R., Chen, C., Chen, X.: Search-mind: training-free multi-modal medical image registration (2026). https:\/\/arxiv.org\/abs\/2604.09743"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Wang, B., Li, R., Chen, C., Chen, X.: Semi-supervised liver segmentation and patch-based fibrosis staging with registration-aided multi-parametric mri. arXiv preprint arXiv:2602.09686 (2026)","DOI":"10.1007\/978-3-032-16271-7_12"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Sam-med3d: a vision foundation model for general-purpose segmentation on volumetric medical images. IEEE Trans. Neural Netw. Learn. Syst. (2025)","DOI":"10.1109\/TNNLS.2025.3586694"},{"issue":"1","key":"9_CR27","doi-asserted-by":"publisher","first-page":"7866","DOI":"10.1038\/s41467-025-62385-7","volume":"16","author":"C Wu","year":"2025","unstructured":"Wu, C., Zhang, X., Zhang, Y., Hui, H., Wang, Y., Xie, W.: Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data. Nat. Commun. 16(1), 7866 (2025)","journal-title":"Nat. Commun."},{"issue":"5","key":"9_CR28","first-page":"6021","volume":"45","author":"F Wu","year":"2023","unstructured":"Wu, F., Zhuang, X.: Minimizing estimated risks on unlabeled data: a new formulation for semi-supervised medical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 6021\u20136036 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"9_CR29","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1148\/radiol.2017170706","volume":"286","author":"K Yasaka","year":"2018","unstructured":"Yasaka, K., Akai, H., Abe, O., Kiryu, S.: Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced ct: a preliminary study. Radiology 286(3), 887\u2013896 (2018)","journal-title":"Radiology"},{"issue":"1","key":"9_CR30","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1148\/radiol.2017171928","volume":"287","author":"K Yasaka","year":"2018","unstructured":"Yasaka, K., Akai, H., Kunimatsu, A., Abe, O., Kiryu, S.: Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase mr images. Radiology 287(1), 146\u2013155 (2018)","journal-title":"Radiology"},{"key":"9_CR31","unstructured":"Zhang, S., et\u00a0al.: Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv preprint arXiv:2303.00915 (2023)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-35387-0_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:15:38Z","timestamp":1792023338000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-35387-0_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"ISBN":["9783032353863","9783032353870"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-35387-0_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]},"assertion":[{"value":"12 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIiH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on AI in Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiih2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aiih.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}