{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T01:35:13Z","timestamp":1743384913785,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819967018"},{"type":"electronic","value":"9789819967025"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-6702-5_7","type":"book-chapter","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T18:02:42Z","timestamp":1700503362000},"page":"85-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Knowledge Distillation Technique for Colonoscopy and Medical Image Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9539-6719","authenticated-orcid":false,"given":"Indrajit","family":"Kar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7932-5307","authenticated-orcid":false,"given":"Sudipta","family":"Mukhopadhyay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1923-2103","authenticated-orcid":false,"given":"Rishabh","family":"Balaiwar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5248-3601","authenticated-orcid":false,"given":"Tanmay","family":"Khule","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"issue":"5","key":"7_CR1","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1049\/ipr2.12419","volume":"16","author":"R Wang","year":"2022","unstructured":"Wang, R., et al.: Medical image segmentation using deep learning: a survey. IET Image Process. 16(5), 1243\u20131267 (2022)","journal-title":"IET Image Process."},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.compmedimag.2018.03.001","volume":"66","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., Oda, H., Zhou, X., Shimizu, N., Yang, Y., Hayashi, Y., et al.: An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput. Med. Imag. Graph. 66, 90\u201399 (2018)","journal-title":"Comput. Med. Imag. Graph."},{"issue":"3","key":"7_CR3","doi-asserted-by":"publisher","first-page":"748","DOI":"10.3390\/s21030748","volume":"21","author":"M Masud","year":"2021","unstructured":"Masud, M., et al.: A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework. Sensors 21(3), 748 (2021)","journal-title":"Sensors"},{"key":"7_CR4","first-page":"348","volume":"128","author":"H Brenner","year":"2014","unstructured":"Brenner, H., Stock, C., Hoffmeister, M.: Effect of screening sigmoidoscopy and screening colonoscopy on colorectal cancer incidence and mortality: systematic review and meta-analysis of randomised controlled trials and observational studies. BMJ 128, 348 (2014)","journal-title":"BMJ"},{"key":"7_CR5","first-page":"CD003430","volume":"1","author":"TK Asano","year":"2002","unstructured":"Asano, T.K., McLeod, R.S.: Dietary fibre for the prevention of colorectal adenomas and carcinomas. Cochrane Database Syst. Rev. 1, CD003430 (2002)","journal-title":"Cochrane Database Syst. Rev."},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"263177452097959","DOI":"10.1177\/2631774520979591","volume":"13","author":"A Sivananthan","year":"2020","unstructured":"Sivananthan, A., Glover, B., Ayaru, L., Patel, K., Darzi, A., Patel, N.: The evolution of lower gastrointestinal endoscopy: where are we now? Therap. Adv. Gastrointest. Endosc. 13, 2631774520979591 (2020)","journal-title":"Therap. Adv. Gastrointest. Endosc."},{"issue":"16","key":"7_CR7","doi-asserted-by":"publisher","first-page":"5630","DOI":"10.3390\/s21165630","volume":"21","author":"CY Eu","year":"2021","unstructured":"Eu, C.Y., Tang, T.B., Lin, C.H., Lee, L.H., Lu, C.K.: Automatic polyp segmentation in colonoscopy images using a modified deep convolutional encoder-decoder architecture. Sensors 21(16), 5630 (2021)","journal-title":"Sensors"},{"key":"7_CR8","unstructured":"Brandao, P.: Enhancing endoscopic navigation and polyp detection using artificial intelligence (Doctoral dissertation, UCL (University College London)) (2021)"},{"issue":"10","key":"7_CR9","doi-asserted-by":"publisher","first-page":"7789","DOI":"10.1109\/JIOT.2020.3039359","volume":"8","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Tao, D.: Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet Things J. 8(10), 7789\u20137817 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"7_CR10","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1109\/TPAMI.2020.3024646","volume":"44","author":"J Feng","year":"2020","unstructured":"Feng, J., Li, S., Li, X., Wu, F., Tian, Q., Yang, M.H., Ling, H.: Taplab: a fast framework for semantic video segmentation tapping into compressed-domain knowledge. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1591\u20131603 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR11","unstructured":"Wang, W., Zhou, T., Porikli, F., Crandall, D., Van Gool, L.: A survey on deep learning techniques for video segmentation. arXiv preprint arXiv:2107.01153 (2021)"},{"key":"7_CR12","unstructured":"Xie, J., Shuai, B., Hu, J.F., Lin, J., Zheng, W.S.: Improving fast segmentation with teacher-student learning. arXiv preprint arXiv:1810.08476 (2018)"},{"key":"7_CR13","unstructured":"Li, G., Yun, I., Kim, J., Kim, J.: Dabnet: depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv preprint arXiv:1907.11357 (2019)"},{"key":"7_CR14","doi-asserted-by":"publisher","first-page":"101923","DOI":"10.1016\/j.artmed.2020.101923","volume":"108","author":"LF Sanchez-Peralta","year":"2020","unstructured":"Sanchez-Peralta, L.F., Bote-Curiel, L., Picon, A., Sanchez-Margallo, F.M., Pagador, J.B.: Deep learning to find colorectal polyps in colonoscopy: a systematic literature review. Artif. Intell. Med. 108, 101923 (2020)","journal-title":"Artif. Intell. Med."},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Kayes, M.I.: A lightweight and robust convolutional neural network for carcinogenic polyp identification (Doctoral dissertation, University of Science and Technology) (2021)","DOI":"10.1109\/ICISET54810.2022.9775824"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Chavarrias-Solano, P.E., Teevno, M.A., Ochoa-Ruiz, G., Ali, S.: Knowledge distillation with a class-aware loss for endoscopic disease detection. In: MICCAI Workshop on Cancer Prevention Through Early Detection, pp. 67\u201376. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-17979-2_7"},{"key":"7_CR17","unstructured":"Sivaprakasam, M.: XP-Net: An Attention Segmentation Network by Dual Teacher Hierarchical Knowledge Distillation for Polyp Generalization (2022)"},{"key":"7_CR18","unstructured":"Kang, J., Gwak, J.: KD-ResUNet++: automatic polyp segmentation via self-knowledge distillation. In: MediaEval (2020)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., Lange, T.D., Johansen, D., Johansen, H.D.: Kvasir-seg: a segmented polyp dataset. In: International Conference on Multimedia Modeling, pp. 451\u2013462. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-37734-2_37"},{"issue":"5","key":"7_CR20","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1111\/1754-9485.13261","volume":"65","author":"P Chlap","year":"2021","unstructured":"Chlap, P., et al.: A review of medical image data augmentation techniques for deep learning applications. J. Med. Imag. Radiat. Oncol. 65(5), 545\u2013563 (2021)","journal-title":"J. Med. Imag. Radiat. Oncol."},{"issue":"1","key":"7_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019)","journal-title":"J. Big Data"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Patel, K., Bur, A.M., Wang, G.: Enhanced U-Net: a feature enhancement network for polyp segmentation. In: Proceedings of the 2021 18th Conference on Robots and Vision (CRV), pp. 181\u2013188. IEEE (2021)","DOI":"10.1109\/CRV52889.2021.00032"},{"key":"7_CR23","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. Springer, Cham (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"7_CR24","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3389\/fncom.2019.00044","volume":"13","author":"DE Cahall","year":"2019","unstructured":"Cahall, D.E., Rasool, G., Bouaynaya, N.C., Fathallah-Shaykh, H.M.: Inception modules enhance brain tumor segmentation. Front. Comput. Neurosci. 13, 44 (2019)","journal-title":"Front. Comput. Neurosci."},{"issue":"26","key":"7_CR25","doi-asserted-by":"publisher","first-page":"37333","DOI":"10.1007\/s11042-021-11334-9","volume":"81","author":"ES Chahal","year":"2022","unstructured":"Chahal, E.S., Patel, A., Gupta, A., Purwar, A.: Unet based exception model for prostate cancer segmentation from MRI images. Multimedia Tools Appl. 81(26), 37333\u201337349 (2022)","journal-title":"Multimedia Tools Appl."},{"key":"7_CR26","unstructured":"Tan, M., Le, Q.: Efficient net: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"}],"container-title":["Smart Innovation, Systems and Technologies","Evolution in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6702-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T07:59:58Z","timestamp":1730534398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6702-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819967018","9789819967025"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6702-5_7","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FICTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Frontiers of Intelligent Computing: Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cardiff","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ficta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ficta.co.uk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}