{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:54:10Z","timestamp":1781675650777,"version":"3.54.5"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032136534","type":"print"},{"value":"9783032136541","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-13654-1_3","type":"book-chapter","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:11:19Z","timestamp":1781673079000},"page":"22-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Fusion for\u00a0Melanoma Classification Using Dermoscopic Images and\u00a0Clinical Metadata"],"prefix":"10.1007","author":[{"given":"Aby","family":"Diallo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mouhamad M.","family":"Allaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dame","family":"Samb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marawan","family":"Elbatel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serigne","family":"Lo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mamadou","family":"Bousso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"3_CR1","unstructured":"World Health Organization: Ultraviolet (UV) radiation and skin cancer. WHO QA (2023). https:\/\/www.who.int\/fr\/news-room\/questions-and-answers\/item\/ultraviolet-(uv)-radiation-and-skin-cancer"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, B., et al.: Ultraviolet radiation and skin cancer: epidemiology, mechanisms and preventive strategies. Front. Oncol. 12, 893972 (2022). https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2022.893972\/full","DOI":"10.3389\/fonc.2022.893972"},{"key":"3_CR3","unstructured":"Liu, Y., Jain, A., Eng, C., et al.: A deep learning system for differential diagnosis of skin diseases. Sci. Transl. Med. 12(563), eabb3652 (2020). https:\/\/www.science.org\/doi\/10.1126\/scitranslmed.abb3652"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Jojoa Acosta, M.F., Caballero Tovar, L.Y., Garcia-Zapirain, M.B., Percybrooks, W.S.: Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Med. Imaging 20(1), 65 (2021). https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-020-00534-8","DOI":"10.1186\/s12880-020-00534-8"},{"key":"3_CR5","unstructured":"International Skin Imaging Collaboration (ISIC): ISIC Challenge (2020). https:\/\/challenge.isic-archive.com\/data\/#2020"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, B., Zeng, A., Pan, D., Wang, R., Zhao, S.: Skin cancer classification with deep learning: a systematic review. Front. Oncol. 12, 893972 (2022)","DOI":"10.3389\/fonc.2022.893972"},{"key":"3_CR7","unstructured":"Soci\u00e9t\u00e9 canadienne du cancer: M\u00e9lanome de la peau : diagnostic (2024). Consult\u00e9 en mai 2025. https:\/\/cancer.ca\/fr\/cancer-information\/cancer-types\/melanoma-skin\/diagnosis"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Dinnes, J., et al.: Dermoscopy, with and without visual inspection, for the diagnosis of melanoma in adults. Cochrane Datab. Syst. Rev. 2018(12), CD011902 (2018). https:\/\/doi.org\/10.1002\/14651858.CD011902.pub2. https:\/\/www.researchgate.net\/publication\/326460219_Dermoscopy_with_and_without_visual_inspection_for_the_diagnosis_of_melanoma_in_adults","DOI":"10.1002\/14651858.CD011902.pub2"},{"key":"3_CR9","unstructured":"Brinker, T.J., et al.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. J. Med. Internet Res. 20(10), e11936 (2018)"},{"key":"3_CR10","doi-asserted-by":"publisher","unstructured":"Stofa, M.M., Zulkifley, M.A., Zainuri, M.A.A.M.: Skin lesions classification and segmentation: a review. Int. J. Adv. Comput. Sci. Appl. 12(10) (2021). https:\/\/doi.org\/10.14569\/ijacsa.2021.0121060. https:\/\/thesai.org\/Publications\/ViewPaper?Code=IJACSA&Issue=10&SerialNo=60&Volume=12","DOI":"10.14569\/ijacsa.2021.0121060"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Efat, A.H., Hasan, S.M.M., Uddin, M.P., Mamun, M.A.: A multi-level ensemble approach for skin lesion classification using customized transfer learning with triple attention. PLoS ONE 19(10), e0309430 (2024)","DOI":"10.1371\/journal.pone.0309430"},{"key":"3_CR12","unstructured":"Viso.ai: Vision Transformer (ViT): How It Works and Why It Matters in Deep Learning (2024). Consult\u00e9 le 2026\/03\/28. https:\/\/viso.ai\/deep-learning\/vision-transformer-vit\/"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Cirrincione, G., et al.: Transformer-based approach to melanoma detection. Sensors 23(12), 5677 (2023)","DOI":"10.3390\/s23125677"},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Yacob, F., et al.: Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images. Sci. Rep. 13, 7555 (2023). https:\/\/doi.org\/10.1038\/s41598-023-33863-z. https:\/\/www.nature.com\/articles\/s41598-023-33863-z","DOI":"10.1038\/s41598-023-33863-z"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, Y., Zhang, Z., Chen, Y.: A novel approach for melanoma detection utilizing GAN synthesis and BatchFormer vision transformer model. Comput. Methods Prog. Biomed. (2024). https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0010482524006577","DOI":"10.1016\/j.compbiomed.2024.108572"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Roy, V.K., Thakur, V., Baliyan, N., Goyal, N., Nijhawan, R.: A framework for seborrheic keratosis skin disease identification using vision transformer. In: Malik, P., Nautiyal, L., Ram, M. (eds.) Machine Learning for Cyber Security, pp. 117\u2013128. De Gruyter (2023). https:\/\/doi.org\/10.1515\/9783110766745-006","DOI":"10.1515\/9783110766745-006"},{"key":"3_CR17","doi-asserted-by":"publisher","unstructured":"Yang, G., Luo, S., Greer, P.: A novel vision transformer model for skin cancer classification. Neural Process. Lett. 55, 9335\u20139351 (2023). https:\/\/doi.org\/10.1007\/s11063-023-11204-5","DOI":"10.1007\/s11063-023-11204-5"},{"key":"3_CR18","doi-asserted-by":"publisher","unstructured":"Vachmanus, S., Noraset, T., Piyanonpong, W., Rattananukrom, T., Tuarob, S.: DeepMetaForge: a deep vision-transformer metadata-fusion network for automatic skin lesion classification. IEEE Access 11 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3345225. https:\/\/www.researchgate.net\/publication\/376674813_DeepMetaForge_A_Deep_Vision-Transformer_Metadata-Fusion_Network_for_Automatic_Skin_Lesion_Classification","DOI":"10.1109\/ACCESS.2023.3345225"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Khan, S., Khan, A.: SkinViT: a transformer based method for Melanoma and Nonmelanoma classification. PLoS ONE 18(12), e0295151 (2023)","DOI":"10.1371\/journal.pone.0295151"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Wang, R., et al.: A novel approach for melanoma detection utilizing GAN synthesis and vision transformer. Comput. Biol. Med. 176, 108572 (2024). https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108572. https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0010482524006577","DOI":"10.1016\/j.compbiomed.2024.108572"},{"key":"3_CR21","doi-asserted-by":"publisher","unstructured":"Catal Reis, H., Turk, V.: Fusion of transformer attention and CNN features for skin cancer detection. Appl. Soft Comput. 164, 112013 (2024). https:\/\/doi.org\/10.1016\/j.asoc.2024.112013. https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1568494624007877","DOI":"10.1016\/j.asoc.2024.112013"},{"issue":"2","key":"3_CR22","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1109\/JBHI.2023.3308697","volume":"28","author":"W Dai","year":"2024","unstructured":"Dai, W., Liu, R., Wu, T., Wang, M., Yin, J., Liu, J.: Deeply supervised skin lesions diagnosis with stage and branch attention. IEEE J. Biomed. Health Inf. 28(2), 719\u2013729 (2024)","journal-title":"IEEE J. Biomed. Health Inf."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing in Resource Constrained Settings"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-13654-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:11:27Z","timestamp":1781673087000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-13654-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032136534","9783032136541"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-13654-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIRASOL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Medical Image Computing in Resource Constrained Settings","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mirasol2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/event.fourwaves.com\/mirasol\/pages","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}