{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:30:23Z","timestamp":1769769023603,"version":"3.49.0"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159892","type":"print"},{"value":"9783032159908","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\/978-3-032-15990-8_3","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:00:03Z","timestamp":1769716803000},"page":"35-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Interpretable Approach to\u00a0Deep Multimodal Data Fusion Applied to\u00a0Cancer Diagnosis"],"prefix":"10.1007","author":[{"given":"Leandro M.","family":"de Lima","sequence":"first","affiliation":[]},{"given":"Matheus B.","family":"Rocha","sequence":"additional","affiliation":[]},{"given":"Renato A.","family":"Krohling","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Afreen, S., Bhurjee, A.K., Aziz, R.M.: Feature selection using game Shapley improved grey wolf optimizer for optimizing cancer classification. Knowl. Inf. Syst., 1\u201332 (2025)","DOI":"10.1007\/s10115-025-02340-6"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Aguirre-Urizar, J.M., de Mendoza, I.L.-I., Warnakulasuriya, S.: Malignant transformation of oral leukoplakia systematic review and meta-analysis of the last 5 years. Oral Dis. 27(8), 1881\u20131895 (2021)","DOI":"10.1111\/odi.13810"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Andrade, J.O.M., Santos, C.A.d.S.T., Oliveira, M.C.: Associated factors with oral cancer: a study of case control in a population of the Brazil\u2019s Northeast. Rev. Bras. Epidemiol. 18, 894\u2013905 (2015)","DOI":"10.1590\/1980-5497201500040017"},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40860-024-00240-0","volume":"11","author":"A Assis","year":"2024","unstructured":"Assis, A., Dantas, J., Andrade, E.: The performance-interpretability trade-off: a comparative study of machine learning models. J. Reliab. Intell. Environ. 11(1), 1 (2024)","journal-title":"J. Reliab. Intell. Environ."},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Ribeiro-de Assis, M.C.F., et al.: NDB-UFES: an oral cancer and leukoplakia dataset composed of histopathological images and patient data. Data Brief 48, 109128 (2023)","DOI":"10.1016\/j.dib.2023.109128"},{"issue":"6","key":"3_CR6","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s00138-021-01249-8","volume":"32","author":"SY Boulahia","year":"2021","unstructured":"Boulahia, S.Y., Amamra, A., Madi, M.R., Daikh, S.: Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition. Mach. Vis. Appl. 32(6), 121 (2021)","journal-title":"Mach. Vis. Appl."},{"issue":"5","key":"3_CR7","first-page":"e603","volume":"24","author":"B del Carmen Miguel\u00e1\u00f1ez-Medr\u00e1n","year":"2019","unstructured":"del Carmen Miguel\u00e1\u00f1ez-Medr\u00e1n, B., Pozo-Kreilinger, J.J., Cebri\u00e1n-Carretero, J.L., Mart\u00ednez-Garc\u00eda, M.\u00c1., L\u00f3pez-S\u00e1nchez, A.F.: Oral squamous cell carcinoma of tongue: histological risk assessment. A pilot study. Med. Oral Patol. Oral Cir. Bucal 24(5), e603 (2019)","journal-title":"Med. Oral Patol. Oral Cir. Bucal"},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"105451","DOI":"10.1016\/j.oraloncology.2021.105451","volume":"121","author":"A Chamoli","year":"2021","unstructured":"Chamoli, A., Gosavi, A.S., Shirwadkar, U.P., et al.: Overview of oral cavity squamous cell carcinoma: risk factors, mechanisms, and diagnostics. Oral Oncol. 121, 105451 (2021)","journal-title":"Oral Oncol."},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"3_CR10","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale (2021)"},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"102536","DOI":"10.1016\/j.inffus.2024.102536","volume":"112","author":"J Duan","year":"2024","unstructured":"Duan, J., Xiong, J., Li, Y., Ding, W.: Deep learning based multimodal biomedical data fusion: an overview and comparative review. Inf. Fus. 112, 102536 (2024)","journal-title":"Inf. Fus."},{"issue":"6","key":"3_CR12","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1684\/ejd.2021.4171","volume":"31","author":"AF Duarte","year":"2021","unstructured":"Duarte, A.F., Sousa-Pinto, B., Azevedo, L.F., Barros, A.M., Puig, S., Malvehy, J., Haneke, E., Correia, O.: Clinical ABCDE rule for early melanoma detection. Eur. J. Dermatol. 31(6), 771\u2013778 (2021)","journal-title":"Eur. J. Dermatol."},{"key":"3_CR13","unstructured":"Dziugaite, G.K., Ben-David, S., Roy, D.M.: Enforcing interpretability and its statistical impacts: trade-offs between accuracy and interpretability. arXiv preprint arXiv:2010.13764 (2020)"},{"issue":"679","key":"3_CR14","doi-asserted-by":"publisher","first-page":"e112","DOI":"10.3399\/bjgp18X700205","volume":"69","author":"C Grafton-Clarke","year":"2019","unstructured":"Grafton-Clarke, C., Chen, K.W., Wilcock, J.: Diagnosis and referral delays in primary care for oral squamous cell cancer: a systematic review. Br. J. Gen. Pract. 69(679), e112\u2013e126 (2019)","journal-title":"Br. J. Gen. Pract."},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision, ECCV 2016. LNCS, vol. 9908. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Heo, B., Yun, S., Han, D., et al.: Rethinking spatial dimensions of vision transformers (2021)","DOI":"10.1109\/ICCV48922.2021.01172"},{"issue":"3","key":"3_CR17","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1177\/0022034520902128","volume":"99","author":"B Ilhan","year":"2020","unstructured":"Ilhan, B., Lin, K., Guneri, P., et al.: Improving oral cancer outcomes with imaging and artificial intelligence. J. Dent. Res. 99(3), 241\u2013248 (2020)","journal-title":"J. Dent. Res."},{"issue":"3","key":"3_CR18","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/S0738-081X(02)00236-5","volume":"20","author":"RH Johr","year":"2002","unstructured":"Johr, R.H.: Dermoscopy: alternative melanocytic algorithms\u2014the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist. Clin. Dermatol. 20(3), 240\u2013247 (2002)","journal-title":"Clin. Dermatol."},{"key":"3_CR19","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, pp. 3147\u20133156. Curran Associates (2017)"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: A review of deep learning-based information fusion techniques for multimodal medical image classification. Comput. Biol. Med. 177, 108635 (2024)","DOI":"10.1016\/j.compbiomed.2024.108635"},{"key":"3_CR21","doi-asserted-by":"publisher","unstructured":"de Lima, L.M., Krohling, R.A.: Exploring advances in transformers and CNN for skin lesion diagnosis on small datasets. In: Xavier-Junior, J.C., Rios, R.A. (eds.) Intelligent Systems, BRACIS 2022. LNCS, vol. 13654. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21689-3_21","DOI":"10.1007\/978-3-031-21689-3_21"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"de Lima, L.M., et al.: Importance of complementary data to histopathological image analysis of oral leukoplakia and carcinoma using deep neural networks. Intell. Med. 3(4), 258\u2013266 (2023)","DOI":"10.1016\/j.imed.2023.01.004"},{"key":"3_CR23","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"2","key":"3_CR24","first-page":"167","volume":"88","author":"AG Marzuka","year":"2015","unstructured":"Marzuka, A.G., Book, S.E.: Basal cell carcinoma: pathogenesis, epidemiology, clinical features, diagnosis, histopathology, and management. Yale J. Biol. Med. 88(2), 167\u2013179 (2015)","journal-title":"Yale J. Biol. Med."},{"key":"3_CR25","unstructured":"National Institute of Cancer Jos\u00e9 Alencar Gomes da Silva (INCA): Cancer incidence in Brazil - 2023 estimates (2023). https:\/\/www.inca.gov.br\/sites\/ufu.sti.inca.local\/files\/media\/document\/estimativa-2023.pdf"},{"key":"3_CR26","unstructured":"Neville, B.: Patologia Oral e Maxilofacial, 4th edn. Elsevier, Brasil (2016). (in Portuguese)"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Pacheco, A.G., et al.: PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Brief 32, 106221 (2020)","DOI":"10.1016\/j.dib.2020.106221"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., et al.: Designing network design spaces (2020)","DOI":"10.1109\/CVPR42600.2020.01044"},{"issue":"1","key":"3_CR29","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s41666-023-00127-4","volume":"7","author":"E Rezk","year":"2023","unstructured":"Rezk, E., Eltorki, M., El-Dakhakhni, W.: Interpretable skin cancer classification based on incremental domain knowledge learning. J. Healthc. Inf. Res. 7(1), 59\u201383 (2023)","journal-title":"J. Healthc. Inf. Res."},{"key":"3_CR30","first-page":"4765","volume":"30","author":"M Scott","year":"2017","unstructured":"Scott, M., Su-In, L., et al.: A unified approach to interpreting model predictions. Adv. Neural. Inf. Process. Syst. 30, 4765\u20134774 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"5","key":"3_CR31","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.3390\/cancers15051421","volume":"15","author":"B Song","year":"2023","unstructured":"Song, B., et al.: Interpretable and reliable oral cancer classifier with attention mechanism and expert knowledge embedding via attention map. Cancers 15(5), 1421 (2023)","journal-title":"Cancers"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Susila, S.J.G., Kavitha, D.: A deep convolutional neural network-based keratitis detection and classification approach. Multimedia Tools Appl. (2025)","DOI":"10.1007\/s11042-025-20714-4"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Tan, Y., et al.: Oral squamous cell carcinomas: state of the field and emerging directions. Int. J. Oral Sci. 15(1), 44 (2023)","DOI":"10.1038\/s41368-023-00249-w"},{"issue":"4","key":"3_CR34","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1111\/odi.14123","volume":"29","author":"S Tovaru","year":"2023","unstructured":"Tovaru, S., et al.: Oral leukoplakia: a clinicopathological study and malignant transformation. Oral Dis. 29(4), 1454\u20131463 (2023)","journal-title":"Oral Dis."},{"issue":"1","key":"3_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","journal-title":"Sci. Data"},{"issue":"4","key":"3_CR36","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1016\/j.joms.2016.10.012","volume":"75","author":"A Villa","year":"2017","unstructured":"Villa, A., Woo, S.B.: Leukoplakia\u2014a diagnostic and management algorithm. J. Oral Maxillofac. Surg. 75(4), 723\u2013734 (2017)","journal-title":"J. Oral Maxillofac. Surg."},{"key":"3_CR37","unstructured":"WHO: WHO report on cancer: setting priorities, investing wisely and providing care for all. World Health Organization (2020)"},{"key":"3_CR38","doi-asserted-by":"publisher","first-page":"893972","DOI":"10.3389\/fonc.2022.893972","volume":"12","author":"Y Wu","year":"2022","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)","journal-title":"Front. Oncol."},{"key":"3_CR39","doi-asserted-by":"crossref","unstructured":"Xu, W., Xu, Y., Chang, T., et al.: Co-scale conv-attentional image transformers (2021)","DOI":"10.1109\/ICCV48922.2021.00983"},{"key":"3_CR40","doi-asserted-by":"publisher","first-page":"102721","DOI":"10.1016\/j.inffus.2024.102721","volume":"115","author":"B Xua","year":"2024","unstructured":"Xua, B., Yang, G.: Interpretability research of deep learning: a literature survey. Inf. Fus. 115, 102721 (2024)","journal-title":"Inf. Fus."},{"issue":"2","key":"3_CR41","doi-asserted-by":"publisher","first-page":"100211","DOI":"10.1016\/j.hcc.2024.100211","volume":"4","author":"Y Yao","year":"2024","unstructured":"Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., Zhang, Y.: A survey on large language model (LLM) security and privacy: the good, the bad, and the ugly. High-Confidence Comput. 4(2), 100211 (2024)","journal-title":"High-Confidence Comput."},{"issue":"5","key":"3_CR42","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1109\/TETCI.2021.3100641","volume":"5","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Ti\u0148o, P., Leonardis, A., Tang, K.: A survey on neural network interpretability. IEEE Trans. Emerg. Top. Comput. Intell. 5(5), 726\u2013742 (2021)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15990-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:00:09Z","timestamp":1769716809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15990-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159892","9783032159908"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15990-8_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":"30 January 2026","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":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","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":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}