{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T02:10:33Z","timestamp":1770689433589,"version":"3.49.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031986932","type":"print"},{"value":"9783031986949","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"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\/978-3-031-98694-9_1","type":"book-chapter","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T05:51:39Z","timestamp":1752472299000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TransE$$^2$$UNet: Edge Guided TransEfficientUNET for\u00a0Generalized Colon Polyp Segmentation from\u00a0Endoscopy Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5572-1056","authenticated-orcid":false,"given":"Subhashis","family":"Kar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0652-1339","authenticated-orcid":false,"given":"Souradeep","family":"Mukhopadhyay","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5916-4108","authenticated-orcid":false,"given":"Shreyan","family":"Kundu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8078-6730","authenticated-orcid":false,"given":"Debesh","family":"Jha","sequence":"additional","affiliation":[]},{"given":"Rammohan","family":"Mallipeddi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"issue":"4","key":"1_CR1","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1053\/j.gastro.2018.06.037","volume":"155","author":"G Urban","year":"2018","unstructured":"Urban, G., et al.: Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 155(4), 1069\u20131078 (2018)","journal-title":"Gastroenterology"},{"issue":"05","key":"1_CR2","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1055\/s-0031-1291666","volume":"44","author":"AM Leufkens","year":"2012","unstructured":"Leufkens, A.M., Van Oijen, M., Vleggaar, F.P., Siersema, P.D.: Factors influencing the miss rate of polyps in a back-to-back colonoscopy study. Endoscopy 44(05), 470\u2013475 (2012)","journal-title":"Endoscopy"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Proceedings of the International Conference on Multimedia Modeling (MMM), pp. 451\u2013462 (2020)","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"1_CR4","unstructured":"Debesh, J., et al.:(2024). PolypDB: a curated multi-center dataset for development of AI algorithms in colonoscopy. 10.48550\/arXiv.2409.00045"},{"key":"1_CR5","unstructured":"Lan, P.N., et al.: NeoUNet: towards accurate colon polyp segmentation and neoplasm detection. arXiv preprint arXiv:2107.05023 (2021)"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Bernal, J., S\u00e1nchez, F. J., Fern\u00e1ndez-Esparrach, G., Gil, D., Rodr\u00edguez, C., Vilari\u00f1o, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99\u2013111 (2015)","DOI":"10.1016\/j.compmedimag.2015.02.007"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., De Lange, T., Johansen, D., Johansen, H.D.: ResUNet++: An advanced architecture for medical image segmentation. In: Proceedings of the International Symposium on Multimedia (ISM), pp. 225\u20132255 (2019)","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"1_CR10","unstructured":"Huang, C.H., Wu, H.Y., Lin, Y.L.: HarDNet-MSEG a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 FPS. arXiv preprint arXiv:2101.07172 (2021)"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: Real-time polyp detection, localization and segmentation in colonoscopy using deep learning. IEEE Access 9, 40496\u201340510 (2021)","DOI":"10.1109\/ACCESS.2021.3063716"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Fan, D.P., et al.: Pranet: parallel reverse attention network for polyp segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 263\u2013273 (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Tomar, N.K., Jha, D., Bagci, U., Ali, S.: TGANet: Text-guided attention for improved polyp segmentation. In: Proceedings of the 25th International Conference on MICCAI, pp. 151\u2013160 (2022)","DOI":"10.1007\/978-3-031-16437-8_15"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Tomar, N.K., Shergill, A., Rieders, B., Bagci, U., Jha, D.: TransResU-Net: a transformer based resu-net for real-time colon polyp segmentation. In: Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1\u20134 (2023)","DOI":"10.1109\/EMBC40787.2023.10340572"},{"key":"1_CR16","unstructured":"Jha, D., Tomar, N.K., Sharma, V., Bagci, U.: TransNetR: transformer-based residual network for polyp segmentation with multi-center out-of-distribution testing. Proceedings of Medical Imaging and Deep Learning (2023)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Jha, D., Tomar, N.K., Bagci, U.: TransRUPNet for improved polyp segmentation. In: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20134 (2023)","DOI":"10.1109\/EMBC53108.2024.10781511"},{"key":"1_CR18","doi-asserted-by":"publisher","unstructured":"Tomar, N.K., et al.: DDANet: dual decoder attention network for automatic polyp segmentation. In: Del Bimbo, A., et al., (eds.) ICPR 2021. LNCS, vol. 12668, pp. 307\u2013314. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68793-9_23","DOI":"10.1007\/978-3-030-68793-9_23"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Lou, A., Guan, S., Ko, H., Loew, M.H.: Caranet: context axial reverse attention network for segmentation of small medical objects. In: Medical Imaging 2022: Image Processing. vol. 12032, pp. 81\u201392. SPIE (2022)","DOI":"10.1117\/12.2611802"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Tomar, N.K., et al.: Fanet: a feedback attention network for improved biomedical image segmentation. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3159394"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Tomar, N.K., Bagci, U., Jha, D.: DilatedSegNet: a deep dilated segmentation network for polyp segmentation. In: Conference on Multimedia Modeling (2022)","DOI":"10.1007\/978-3-031-27077-2_26"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Marculescu, R.: Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6222\u20136231 (2023)","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Tang, F., et al.: Duat: Dual-aggregation transformer network for medical image segmentation. In: Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 343\u2013356 (2023)","DOI":"10.1007\/978-981-99-8469-5_27"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Shi, W., Xu, J., Gao, P.: Ssformer: a lightweight transformer for semantic segmentation. In: Proceedings of the IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), pp. 1\u20135 (2022)","DOI":"10.1109\/MMSP55362.2022.9949177"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Leufkens, A.M., Van Oijen, M.G., Vleggaar, F.P., Siersema, P.D.: 1178 Factors affecting miss rate of polyps during colonoscopy: results from a prospective, multicenter back-to-back colonoscopy study. Gastrointestinal Endoscopy 73(4), AB165 (2011)","DOI":"10.1016\/j.gie.2011.03.157"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Jha, D., Riegler, M.A., Johansen, D., Halvorsen, P., Johansen, H.D.: Doubleu-net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 558\u2013564 (2020)","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: Nanonet: real-time polyp segmentation in video capsule endoscopy and colonoscopy. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 37\u201343 (2021)","DOI":"10.1109\/CBMS52027.2021.00014"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-98694-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T07:47:57Z","timestamp":1757231277000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-98694-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"ISBN":["9783031986932","9783031986949"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-98694-9_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,15]]},"assertion":[{"value":"15 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leeds","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.leeds.ac.uk\/miua\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}