{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T06:40:02Z","timestamp":1752475202494,"version":"3.41.2"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031986901","type":"print"},{"value":"9783031986918","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\/978-3-031-98691-8_23","type":"book-chapter","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T06:09:47Z","timestamp":1752473387000},"page":"317-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EfficientDet with\u00a0Knowledge Distillation and\u00a0Instance Whitening for\u00a0Real-Time and\u00a0Generalisable Polyp Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1145-8152","authenticated-orcid":false,"given":"Raneem","family":"Toman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3603-0861","authenticated-orcid":false,"given":"Venkataraman","family":"Subramanian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1313-3542","authenticated-orcid":false,"given":"Sharib","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","unstructured":"Ali, S.: Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions 5(1), 1\u201313. https:\/\/doi.org\/10.1038\/s41746-022-00733-3","DOI":"10.1038\/s41746-022-00733-3"},{"key":"23_CR2","doi-asserted-by":"publisher","unstructured":"Ali, S., et al.: A multi-centre polyp detection and segmentation dataset for generalisability assessment 10(1), 75. https:\/\/doi.org\/10.1038\/s41597-023-01981-y","DOI":"10.1038\/s41597-023-01981-y"},{"key":"23_CR3","doi-asserted-by":"publisher","unstructured":"Bar, O., et al.: Impact of data on generalization of AI for surgical intelligence applications 10(1), 22208. https:\/\/doi.org\/10.1038\/s41598-020-79173-6","DOI":"10.1038\/s41598-020-79173-6"},{"key":"23_CR4","doi-asserted-by":"publisher","unstructured":"Bian, H., Jiang, M., Qian, J.: The investigation of constraints in implementing robust AI colorectal polyp detection for sustainable healthcare system 18(7), e0288376. https:\/\/doi.org\/10.1371\/journal.pone.0288376","DOI":"10.1371\/journal.pone.0288376"},{"key":"23_CR5","doi-asserted-by":"publisher","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. https:\/\/doi.org\/10.48550\/arXiv.2005.12872. http:\/\/arxiv.org\/abs\/2005.12872","DOI":"10.48550\/arXiv.2005.12872"},{"key":"23_CR6","doi-asserted-by":"publisher","unstructured":"Carrinho, P., Falcao, G.: Highly accurate and fast YOLOv4-based polyp detection 232, 120834. https:\/\/doi.org\/10.1016\/j.eswa.2023.120834","DOI":"10.1016\/j.eswa.2023.120834"},{"key":"23_CR7","doi-asserted-by":"publisher","unstructured":"Chavarrias-Solanon, P.E., Ali-Teevno, M., Ochoa-Ruiz, G., Ali, S.: Knowledge distillation with a class-aware loss for endoscopic disease detection. https:\/\/doi.org\/10.48550\/arXiv.2207.09530","DOI":"10.48550\/arXiv.2207.09530"},{"key":"23_CR8","unstructured":"Choi, S., Jung, S., Yun, H., Kim, J., Kim, S., Choo, J.: RobustNet: improving domain generalization in urban-scene segmentation via instance selective whitening. http:\/\/arxiv.org\/abs\/2103.15597"},{"key":"23_CR9","doi-asserted-by":"publisher","unstructured":"Gammulle, H., Chen, Y., Sridharan, S., Klein, T., Fookes, C.: Learning through guidance: knowledge distillation for endoscopic image classification. https:\/\/doi.org\/10.48550\/arXiv.2308.08731","DOI":"10.48550\/arXiv.2308.08731"},{"issue":"6","key":"23_CR10","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129(6), 1789\u20131819 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01453-z","journal-title":"Int. J. Comput. Vision"},{"key":"23_CR11","doi-asserted-by":"publisher","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. https:\/\/doi.org\/10.48550\/arXiv.1503.02531","DOI":"10.48550\/arXiv.1503.02531"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Huang, J., et al.: Speed\/accuracy trade-offs for modern convolutional object detectors. https:\/\/doi.org\/10.48550\/arXiv.1611.10012","DOI":"10.48550\/arXiv.1611.10012"},{"key":"23_CR13","doi-asserted-by":"publisher","unstructured":"Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. https:\/\/doi.org\/10.48550\/arXiv.1911.07069","DOI":"10.48550\/arXiv.1911.07069"},{"key":"23_CR14","doi-asserted-by":"publisher","unstructured":"Ji, G.P., Zhang, J., Campbell, D., Xiong, H., Barnes, N.: Rethinking polyp segmentation from an out-of-distribution perspective 21(4), 631\u2013639. https:\/\/doi.org\/10.1007\/s11633-023-1472-2","DOI":"10.1007\/s11633-023-1472-2"},{"key":"23_CR15","doi-asserted-by":"publisher","unstructured":"Jiang, Y., Zhang, Z., Zhang, R., Li, G., Cui, S., Li, Z.: YONA: you only need one adjacent reference-frame for accurate and fast video polyp detection. https:\/\/doi.org\/10.48550\/arXiv.2306.03686","DOI":"10.48550\/arXiv.2306.03686"},{"key":"23_CR16","doi-asserted-by":"publisher","unstructured":"Lima, A.C.D.M., et al.: A two-stage method for polyp detection in colonoscopy images based on saliency object extraction and transformers 11, 76108\u201376119. https:\/\/doi.org\/10.1109\/ACCESS.2023.3297097","DOI":"10.1109\/ACCESS.2023.3297097"},{"key":"23_CR17","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. https:\/\/doi.org\/10.48550\/arXiv.1405.0312","DOI":"10.48550\/arXiv.1405.0312"},{"key":"23_CR18","doi-asserted-by":"publisher","unstructured":"Morgan, E., et al.: Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN 72(2), 338\u2013344. https:\/\/doi.org\/10.1136\/gutjnl-2022-327736","DOI":"10.1136\/gutjnl-2022-327736"},{"key":"23_CR19","unstructured":"Ragu, Raj, A., Rahul, G.S., Chand, S., Preejith, S., Sivaprakasam, M.: XP-NET: An attention segmentation network by dual teacher hierarchical knowledge distillation for polyp generalization"},{"key":"23_CR20","doi-asserted-by":"publisher","unstructured":"Shah, S.C., Itzkowitz, S.H.: Colorectal cancer in inflammatory bowel disease: mechanisms and management 162(3), 715\u2013730.e3. https:\/\/doi.org\/10.1053\/j.gastro.2021.10.035","DOI":"10.1053\/j.gastro.2021.10.035"},{"key":"23_CR21","doi-asserted-by":"publisher","unstructured":"Shehzadi, T., Hashmi, K.A., Stricker, D., Afzal, M.Z.: Object detection with transformers: a review. https:\/\/doi.org\/10.48550\/arXiv.2306.04670","DOI":"10.48550\/arXiv.2306.04670"},{"key":"23_CR22","doi-asserted-by":"publisher","unstructured":"Shin, Y., Balasingham, I.: Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3277\u20133280. https:\/\/doi.org\/10.1109\/EMBC.2017.8037556. ISSN: 1558-4615","DOI":"10.1109\/EMBC.2017.8037556"},{"key":"23_CR23","doi-asserted-by":"publisher","unstructured":"Shine, R., Bui, A., Burgess, A.: Quality indicators in colonoscopy: an evolving paradigm 90(3), 215\u2013221. https:\/\/doi.org\/10.1111\/ans.15775","DOI":"10.1111\/ans.15775"},{"key":"23_CR24","doi-asserted-by":"publisher","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. https:\/\/doi.org\/10.48550\/arXiv.1905.11946","DOI":"10.48550\/arXiv.1905.11946"},{"key":"23_CR25","doi-asserted-by":"publisher","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10778\u201310787. IEEE. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"23_CR26","doi-asserted-by":"publisher","unstructured":"Zaidi, S.S.A., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B.: A survey of modern deep learning based object detection models. https:\/\/doi.org\/10.48550\/arXiv.2104.11892","DOI":"10.48550\/arXiv.2104.11892"},{"key":"23_CR27","doi-asserted-by":"publisher","unstructured":"Zhao, S., et al.: Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis 156(6), 1661\u20131674.e11. https:\/\/doi.org\/10.1053\/j.gastro.2019.01.260","DOI":"10.1053\/j.gastro.2019.01.260"},{"key":"23_CR28","doi-asserted-by":"publisher","unstructured":"Ali, M., Ochoa-Ruiz, G., Ali, S.: A semi-supervised teacher-student framework for surgical tool detection and localization, 11(4), 1033\u20131041 (2023). https:\/\/doi.org\/10.1080\/21681163.2022.2150688","DOI":"10.1080\/21681163.2022.2150688"}],"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\/978-3-031-98691-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T06:09:50Z","timestamp":1752473390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-98691-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"ISBN":["9783031986901","9783031986918"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-98691-8_23","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"}}]}}