{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T20:04:26Z","timestamp":1784145866851,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819235063","type":"print"},{"value":"9789819235070","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"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\/978-981-92-3507-0_40","type":"book-chapter","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T19:36:31Z","timestamp":1784144191000},"page":"480-492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ORCA: Orthogonal Residual Cross-Modal Adaptation for Choroidal Thickness Estimation From Multimodal Fundus Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2075-7725","authenticated-orcid":false,"given":"Peijia","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong Seng","family":"Gan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Metin","family":"S\u00fcleymanzade","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ceren Durmaz","family":"Engin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giray","family":"Ers\u00f6z","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"issue":"3","key":"40_CR1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0301467","volume":"19","author":"Y Arai","year":"2024","unstructured":"Arai, Y., et al.: Predicting central choroidal thickness from colour fundus photographs using deep learning. PLoS One. 19(3), e0301467 (2024)","journal-title":"PLoS One"},{"issue":"4","key":"40_CR2","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s00417-018-04216-8","volume":"257","author":"V Bouteleux","year":"2019","unstructured":"Bouteleux, V., et al.: Increased choroidal thickness: a new feature to monitor age-related macular degeneration recurrence. Graefes Arch. Clin. Exp. Ophthalmol. 257(4), 699\u2013707 (2019)","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Chen, S., et al.: AdaptFormer: adapting vision transformers for scalable visual recognition. arXiv Preprint arXiv:2205.13535. (2022)","DOI":"10.52202\/068431-1212"},{"key":"40_CR4","first-page":"3359","volume-title":"In: 2023 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW)","author":"T Chen","year":"2023","unstructured":"Chen, T., et al.: SAM-adapter: adapting segment anything in underperformed scenes. In: In: 2023 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3359\u20133367. IEEE, Paris, France (2023)"},{"issue":"8","key":"40_CR5","first-page":"386","volume":"66","author":"BG Chuter","year":"2025","unstructured":"Chuter, B.G., et al.: A multimodal implementation of RETFound using optic nerve head fundus photographs and OCT imaging for glaucoma detection. Invest. Ophthalmol. Vis. Sci. 66(8), 386\u2013386 (2025)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gao, Y.: TransMed: transformers advance multi-modal medical image classification. arXiv Preprint arXiv:2103.05940. (2021)","DOI":"10.3390\/diagnostics11081384"},{"key":"40_CR7","doi-asserted-by":"publisher","DOI":"10.3389\/fcell.2021.653692","volume":"9","author":"L Dong","year":"2021","unstructured":"Dong, L., et al.: Deep learning-based estimation of axial length and Subfoveal choroidal thickness from color fundus photographs. Front. Cell Dev. Biol. 9, 653692 (2021)","journal-title":"Front. Cell Dev. Biol."},{"key":"40_CR8","unstructured":"Dutt, R., et al.: Parameter-efficient fine-tuning for medical image analysis: the missed opportunity. arXiv Preprint arXiv:2305.08252. (2024)"},{"issue":"8","key":"40_CR9","doi-asserted-by":"publisher","first-page":"2125","DOI":"10.1007\/s40123-024-00981-4","volume":"13","author":"A Grzybowski","year":"2024","unstructured":"Grzybowski, A., et al.: Retina fundus photograph-based artificial intelligence algorithms in medicine: a systematic review. Ophthalmol Therapy. 13(8), 2125\u20132149 (2024)","journal-title":"Ophthalmol Therapy"},{"issue":"1","key":"40_CR10","first-page":"5336373","volume":"2022","author":"RK Hapsari","year":"2022","unstructured":"Hapsari, R.K., et al.: Modified gray-level Haralick texture features for early detection of diabetes mellitus and high cholesterol with iris image. Int. J. Biomed. Imag. 2022(1), 5336373 (2022)","journal-title":"Int. J. Biomed. Imag."},{"issue":"1","key":"40_CR11","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.survophthal.2024.09.011","volume":"70","author":"E Hoven","year":"2025","unstructured":"Hoven, E., et al.: Choroidal thickness after anti-vascular endothelial growth factor in typical Neovascular age-related macular degeneration: a systematic review and meta-analysis. Surv. Ophthalmol. 70(1), 86\u201395 (2025)","journal-title":"Surv. Ophthalmol."},{"key":"40_CR12","volume-title":"International Conference on Learning Representations","author":"EJ Hu","year":"2022","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: International Conference on Learning Representations (2022)"},{"issue":"1","key":"40_CR13","doi-asserted-by":"publisher","first-page":"5640","DOI":"10.1038\/s41598-020-62347-7","volume":"10","author":"Y Komuku","year":"2020","unstructured":"Komuku, Y., et al.: Choroidal thickness estimation from colour fundus photographs by adaptive Binarisation and deep learning, according to central serous Chorioretinopathy status. Sci. Rep. 10(1), 5640 (2020)","journal-title":"Sci. Rep."},{"key":"40_CR14","unstructured":"Li, H., et al.: Fundus image quality assessment and enhancement: a systematic review. arXiv Preprint arXiv:2501.11520. (2025)"},{"key":"40_CR15","unstructured":"Li, Y., et al.: A review of deep learning-based information fusion techniques for multimodal medical image classification. arXiv Preprint arXiv:2404.15022. (2024)"},{"issue":"1","key":"40_CR16","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s40942-024-00607-8","volume":"10","author":"M Motamed Shariati","year":"2024","unstructured":"Motamed Shariati, M., et al.: Choroidal vascularity index in health and systemic diseases: a systematic review. Int. J. Retina Vitreous. 10(1), 87 (2024)","journal-title":"Int. J. Retina Vitreous"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Peng, X., et al.: Balanced multimodal learning via on-the-fly gradient modulation. arXiv Preprint arXiv:2203.15332. (2022)","DOI":"10.1109\/CVPR52688.2022.00806"},{"key":"40_CR18","series-title":"Information and Data Processing (ICSIDP)","first-page":"1","volume-title":"2024 IEEE International Conference on Signal","author":"Y Rong","year":"2024","unstructured":"Rong, Y., et al.: Choroidal thickness estimation based on fundus images with uncertainty quantification. In: 2024 IEEE International Conference on Signal Information and Data Processing (ICSIDP), pp. 1\u20136. IEEE, Zhuhai, China (2024)"},{"issue":"5","key":"40_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e26872","volume":"10","author":"Y Rong","year":"2024","unstructured":"Rong, Y., et al.: Regional choroidal thickness estimation from color fundus images based on convolutional neural networks. Heliyon. 10(5), e26872 (2024)","journal-title":"Heliyon."},{"key":"40_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2020.100893","volume":"81","author":"S Schmitz-Valckenberg","year":"2021","unstructured":"Schmitz-Valckenberg, S., et al.: Fundus Autofluorescence Imaging. Prog. Retin. Eye Res. 81, 100893 (2021)","journal-title":"Prog. Retin. Eye Res."},{"issue":"1","key":"40_CR21","doi-asserted-by":"publisher","first-page":"26802","DOI":"10.1038\/s41598-024-78515-y","volume":"14","author":"E S\u00fckei","year":"2024","unstructured":"S\u00fckei, E., et al.: Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions. Sci. Rep. 14(1), 26802 (2024)","journal-title":"Sci. Rep."},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Sung, Y.-L., et al.: VL-adapter: parameter-efficient transfer learning for vision-and-language tasks. arXiv Preprint arXiv:2112.06825. (2022)","DOI":"10.1109\/CVPR52688.2022.00516"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Multimodal token fusion for vision transformers. arXiv Preprint arXiv:2204.08721. (2022)","DOI":"10.1109\/CVPR52688.2022.01187"},{"key":"40_CR24","unstructured":"Wei, Y., Hu, D.: MMPareto: boosting multimodal learning with innocent unimodal assistance. arXiv Preprint arXiv:2405.17730. (2024)"},{"key":"40_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128740","volume":"613","author":"Q Xia","year":"2025","unstructured":"Xia, Q., et al.: A comprehensive review of deep learning for medical image segmentation. Neurocomputing. 613, 128740 (2025)","journal-title":"Neurocomputing"},{"key":"40_CR26","unstructured":"Xu, P., et al.: Multimodal learning with transformers: a survey. arXiv Preprint arXiv:2206.06488. (2023)"},{"issue":"6","key":"40_CR27","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1111\/bcp.15237","volume":"88","author":"SC Yeung","year":"2022","unstructured":"Yeung, S.C., et al.: The effect of systemic and topical ophthalmic medications on choroidal thickness: a review. Br. J. Clin. Pharmacol. 88(6), 2673\u20132685 (2022)","journal-title":"Br. J. Clin. Pharmacol."},{"issue":"12","key":"40_CR28","doi-asserted-by":"publisher","first-page":"14679","DOI":"10.1109\/TITS.2023.3300537","volume":"24","author":"J Zhang","year":"2023","unstructured":"Zhang, J., et al.: CMX: cross-modal fusion for RGB-X semantic segmentation with transformers. IEEE Trans. Intell. Transp. Syst. 24(12), 14679\u201314694 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"7981","key":"40_CR29","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","volume":"622","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., et al.: A foundation model for generalizable disease detection from retinal images. Nature. 622(7981), 156\u2013163 (2023)","journal-title":"Nature"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3507-0_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T19:36:34Z","timestamp":1784144194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3507-0_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"ISBN":["9789819235063","9789819235070"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3507-0_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]},"assertion":[{"value":"16 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}