{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T04:06:48Z","timestamp":1751947608270,"version":"3.41.2"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100006182","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e Inova\u00e7\u00e3o do Esp\u00edrito Santo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006182","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Brazilian National Program of Genomics and Precision Health"},{"name":"Brazilian National Program of Genomics and Precision Health"},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s00521-025-11299-y","type":"journal-article","created":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T06:52:51Z","timestamp":1748155971000},"page":"15535-15546","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TransConv: a lightweight architecture based on transformers and convolutional neural networks for adenocarcinoma and Barrett\u2019s esophagus identification"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7060-6097","authenticated-orcid":false,"suffix":"Jr.","given":"Luis A.","family":"Souza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9 G. C.","family":"Pacheco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto F. De","family":"Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thiago","family":"Oliveira-Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudine","family":"Badue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph","family":"Palm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo\u00e3o Paulo","family":"Papa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,25]]},"reference":[{"key":"11299_CR1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.compbiomed.2018.03.014","volume":"96","author":"LA Souza Jr","year":"2018","unstructured":"Souza LA Jr, Palm C, Mendel R, Hook C, Ebigbo A, Probst A, Messmann H, Weber S, Papa JP (2018) A survey on barrett\u2019s esophagus analysis using machine learning. Comput Biol Med 96:203\u2013213. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.03.014","journal-title":"Comput Biol Med"},{"key":"11299_CR2","doi-asserted-by":"crossref","unstructured":"Souza Jr LA, Hook C, Papa JP, Palm (2017) Barrett\u2019s esophagus analysis using SURF features. In: Bildverarbeitung F\u00fcr die Medizin (BVM), pp. 141\u2013146","DOI":"10.1007\/978-3-662-54345-0_34"},{"key":"11299_CR3","doi-asserted-by":"crossref","unstructured":"Souza Jr LA, Afonso LCS, Palm C, Papa JP (2017) Barrett\u2019s esophagus identification using optimum-path forest. In: Conference on graphics, patterns and images (SIBGRAPI), pp. 308\u2013314","DOI":"10.1109\/SIBGRAPI.2017.47"},{"key":"11299_CR4","doi-asserted-by":"publisher","unstructured":"Souza Jr LA, Ebigbo A, Probst A, Messmann H, Papa JP, Mendel R, Palm C (2018) Barrett\u2019s esophagus identification using color co-occurrence matrices. In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 166\u2013173. https:\/\/doi.org\/10.1109\/SIBGRAPI.2018.00028","DOI":"10.1109\/SIBGRAPI.2018.00028"},{"key":"11299_CR5","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.jvcir.2019.01.043","volume":"59","author":"LA Passos","year":"2019","unstructured":"Passos LA, Souza LA Jr, Mendel R, Ebigbo A, Probst A, Messmann H, Palm C, Papa JP (2019) Barrett\u2019s esophagus analysis using infinity restricted boltzmann machines. J Vis Commun Image Represent 59:475\u2013485","journal-title":"J Vis Commun Image Represent"},{"issue":"7","key":"11299_CR6","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1136\/gutjnl-2018-317573","volume":"68","author":"A Ebigbo","year":"2019","unstructured":"Ebigbo A, Mendel R, Probst A, Manzeneder J, Souza LA Jr, Papa JP, Palm C, Messmann H (2019) Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut 68(7):1143\u20131145. https:\/\/doi.org\/10.1136\/gutjnl-2018-317573","journal-title":"Gut"},{"key":"11299_CR7","doi-asserted-by":"publisher","first-page":"104029","DOI":"10.1016\/j.compbiomed.2020.104029","volume":"126","author":"LA Souza Jr","year":"2020","unstructured":"Souza LA Jr, Passos LA, Mendel R, Ebigbo A, Probst A, Messmann H, Palm C, Papa JP (2020) Assisting barrett\u2019s esophagus identification using endoscopic data augmentation based on generative adversarial networks. Comput Biol Med 126:104029","journal-title":"Comput Biol Med"},{"key":"11299_CR8","doi-asserted-by":"publisher","first-page":"104578","DOI":"10.1016\/j.compbiomed.2021.104578","volume":"135","author":"LA Souza Jr","year":"2021","unstructured":"Souza LA Jr, Mendel R, Strasser S, Ebigbo A, Probst A, Messmann H, Papa JP, Palm C (2021) Convolutional neural networks for the evaluation of cancer in barrett\u2019s esophagus: Explainable ai to lighten up the black-box. Comput Biol Med 135:104578","journal-title":"Comput Biol Med"},{"key":"11299_CR9","doi-asserted-by":"crossref","unstructured":"Souza Jr LA, Passos LA, Mendel R, Ebigbo A, Probst A, Messmann H, Palm C, Papa JP (2021) Fine-tuning generative adversarial networks using metaheuristics. In: Bildverarbeitung F\u00fcr die Medizin (BVM), pp. 205\u2013210","DOI":"10.1007\/978-3-658-33198-6_50"},{"key":"11299_CR10","doi-asserted-by":"publisher","unstructured":"Souza LA Jr, Pacheco AC, Passos LA, Santana MCS, Mendel R, Ebigbo A, Probst A, Messmann H, Palm C, Papa JP (2024) Deepcraftfuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with barrett\u2019s esophagus. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-024-09615-z","DOI":"10.1007\/s00521-024-09615-z"},{"issue":"4","key":"11299_CR11","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1136\/gutjnl-2019-319460","volume":"69","author":"A Ebigbo","year":"2020","unstructured":"Ebigbo A, Mendel R, Probst A, Manzeneder J, Prinz F, Souza LA Jr, Papa J, Palm C, Messmann H (2020) Real-time use of artificial intelligence in the evaluation of cancer in Barrett\u2019s oesophagus. Gut 69(4):615\u2013616. https:\/\/doi.org\/10.1136\/gutjnl-2019-319460","journal-title":"Gut"},{"key":"11299_CR12","doi-asserted-by":"crossref","unstructured":"Mendel R, Ebigbo A, Probst A, Messmann H, Palm C (2017) Barrett\u2019s esophagus analysis using convolutional neural networks. In: Bildverarbeitung F\u00fcr die Medizin (BVM) 2017, pp. 80\u201385","DOI":"10.1007\/978-3-662-54345-0_23"},{"key":"11299_CR13","doi-asserted-by":"publisher","first-page":"102092","DOI":"10.1016\/j.media.2021.102092","volume":"72","author":"W Hou","year":"2021","unstructured":"Hou W, Wang L, Cai S, Lin Z, Yu R, Qin J (2021) Early neoplasia identification in barrett\u2019s esophagus via attentive hierarchical aggregation and self-distillation. Med Image Anal 72:102092","journal-title":"Med Image Anal"},{"key":"11299_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00371-021-02136-z","volume":"38","author":"M Hu","year":"2022","unstructured":"Hu M, Ge P, Wang X, Lin H, Ren F (2022) A spatio-temporal integrated model based on local and global features for video expression recognition. Vis Comput 38:1\u201318. https:\/\/doi.org\/10.1007\/s00371-021-02136-z","journal-title":"Vis Comput"},{"key":"11299_CR15","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. CoRR arXiv:abs\/1512.03385"},{"key":"11299_CR16","doi-asserted-by":"crossref","unstructured":"Kusters CHJ, Boers TGW, Jukema JB, Jong MR, Fockens KN, de Groof AJ, Bergman JJ, van\u00a0der Sommen F, de With PHN (2023) Real-time Barrett\u2019s neoplasia characterization in NBI videos using an int8-based quantized neural network. In: Iftekharuddin, K.M., Chen, W. (eds.) Medical Imaging 2023: Computer-Aided Diagnosis, vol. 12465, p. 124651. SPIE. International Society for Optics and Photonics","DOI":"10.1117\/12.2647557"},{"key":"11299_CR17","doi-asserted-by":"crossref","unstructured":"Boers TGW, Kusters CHJ, Fockens KN, Jukema JB, Jong MR, de Groof J, Bergman JJ, van\u00a0der Sommen F, de With PHN (2023) Barrett\u2019s lesion detection using a minimal integer-based neural network for embedded systems integration. In: Iftekharuddin, K.M., Chen, W. (eds.) Medical Imaging 2023: Computer-Aided Diagnosis, vol. 12465, p. 1246527. SPIE. International Society for Optics and Photonics","DOI":"10.1117\/12.2653890"},{"key":"11299_CR18","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An image is worth 16x16 words: Transformers for image recognition at scale. CoRR arXiv:abs\/2010.11929"},{"issue":"6","key":"11299_CR19","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1(6):80\u201383","journal-title":"Biometrics Bull"},{"key":"11299_CR20","doi-asserted-by":"publisher","first-page":"3355","DOI":"10.1007\/s11517-024-03142-8","volume":"62","author":"LA Souza Jr","year":"2024","unstructured":"Souza LA Jr, Passos LA, Santana M, Mendel R, Rauber D, Ebigbo A, Probst A, Messmann H, Papa JP, Palm C (2024) Layer-selective deep representation to improve esophageal cancer classification. Med Biol Eng Comput 62:3355\u20133372. https:\/\/doi.org\/10.1007\/s11517-024-03142-8","journal-title":"Med Biol Eng Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11299-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11299-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11299-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T11:06:48Z","timestamp":1751886408000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11299-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,25]]},"references-count":20,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["11299"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11299-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,5,25]]},"assertion":[{"value":"26 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}