{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:57:30Z","timestamp":1742918250355,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819714162"},{"type":"electronic","value":"9789819714179"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-1417-9_4","type":"book-chapter","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T07:05:03Z","timestamp":1716275103000},"page":"41-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Generalizability of\u00a0Deep Learning Polyp Segmentation Using Online Spatial Interpolation and\u00a0Hue Transformation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6340-8183","authenticated-orcid":false,"given":"Mahmood","family":"Haithami","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7749-7911","authenticated-orcid":false,"given":"Amr","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5165-4539","authenticated-orcid":false,"given":"Iman Yi","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,22]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","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)","journal-title":"Comput. Med. Imaging Graph."},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"80575","DOI":"10.1109\/ACCESS.2022.3195241","volume":"10","author":"NT Duc","year":"2022","unstructured":"Duc, N.T., Oanh, N.T., Thuy, N.T., Triet, T.M., Dinh, V.S.: ColonFormer: an efficient transformer based method for colon polyp segmentation. IEEE Access 10, 80575\u201380586 (2022)","journal-title":"IEEE Access"},{"key":"4_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-59725-2_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"D-P Fan","year":"2020","unstructured":"Fan, D.-P., et al.: PraNet: parallel reverse attention network for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 263\u2013273. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_26"},{"issue":"12","key":"4_CR4","doi-asserted-by":"publisher","first-page":"5666","DOI":"10.1002\/mp.13865","volume":"46","author":"X Guo","year":"2019","unstructured":"Guo, X., Zhang, N., Guo, J., Zhang, H., Hao, Y., Hang, J.: Automated polyp segmentation for colonoscopy images: a method based on convolutional neural networks and ensemble learning. Med. Phys. 46(12), 5666\u20135676 (2019)","journal-title":"Med. Phys."},{"issue":"1","key":"4_CR5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.5617\/nmi.9131","volume":"1","author":"M Haithami","year":"2021","unstructured":"Haithami, M., Ahmed, A., Liao, I.Y., Jalab, H.: Employing GRU to combine feature maps in DeeplabV3 for a better segmentation model. Nordic Mach. Intell. 1(1), 29\u201331 (2021)","journal-title":"Nordic Mach. Intell."},{"key":"4_CR6","unstructured":"Haithami, M., Ahmed, A., Liao, I.Y., Jalab, H.A.: An embedded recurrent neural network-based model for endoscopic semantic segmentation. In: EndoCV@ ISBI, pp. 59\u201368 (2021)"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Howard, A., et\u00a0al.: Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"4_CR8","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":"4_CR9","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. IEEE (2020)","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"4_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/978-3-030-37734-2_37","volume-title":"MultiMedia Modeling","author":"D Jha","year":"2020","unstructured":"Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451\u2013462. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37734-2_37"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Jia, X., et al.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17 (2020)","DOI":"10.1109\/TASE.2020.2964827"},{"key":"4_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"4_CR13","unstructured":"Kvinge, H., Emerson, T.H., Jorgenson, G., Vasquez, S., Doster, T., Lew, J.D.: In what ways are deep neural networks invariant and how should we measure this? arXiv preprint arXiv:2210.03773 (2022)"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"104119","DOI":"10.1016\/j.compbiomed.2020.104119","volume":"128","author":"T Mahmud","year":"2021","unstructured":"Mahmud, T., Paul, B., Fattah, S.A.: PolypSegNet: a modified encoder-decoder architecture for automated polyp segmentation from colonoscopy images. Comput. Biol. Med. 128, 104119 (2021)","journal-title":"Comput. Biol. Med."},{"key":"4_CR16","doi-asserted-by":"publisher","first-page":"33795","DOI":"10.1109\/ACCESS.2019.2904094","volume":"7","author":"NQ Nguyen","year":"2019","unstructured":"Nguyen, N.Q., Lee, S.W.: Robust boundary segmentation in medical images using a consecutive deep encoder-decoder network. IEEE Access 7, 33795\u201333808 (2019)","journal-title":"IEEE Access"},{"key":"4_CR17","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9, 283\u2013293 (2014)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Thambawita, V., et al.: SinGAN-Seg: synthetic training data generation for medical image segmentation. PloS One 17(5), e0267976 (2022)","DOI":"10.1371\/journal.pone.0267976"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Thambawita, V.L., Str\u00fcmke, I., Hicks, S., Riegler, M.A., Halvorsen, P., Parasa, S.: ID: 3523524 data augmentation using generative adversarial networks for creating realistic artificial colon polyp images: validation study by endoscopists. Gastrointest. Endosc. 93(6), AB190 (2021)","DOI":"10.1016\/j.gie.2021.03.431"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"V\u00e1zquez, D., et\u00a0al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthc. Eng. 2017 (2017)","DOI":"10.1155\/2017\/4037190"}],"container-title":["Lecture Notes in Computer Science","Advances in Brain Inspired Cognitive Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-1417-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T07:06:09Z","timestamp":1716275169000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-1417-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819714162","9789819714179"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-1417-9_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Inspired Cognitive Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kuala Lumpur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaysia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bics2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}