{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T09:04:34Z","timestamp":1770973474070,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032065926","type":"print"},{"value":"9783032065933","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"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\/978-3-032-06593-3_1","type":"book-chapter","created":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T12:37:55Z","timestamp":1759063075000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MEGAN: Mixture of\u00a0Experts for\u00a0Robust Uncertainty Estimation in\u00a0Endoscopy Videos"],"prefix":"10.1007","author":[{"given":"Damola","family":"Agbelese","sequence":"first","affiliation":[]},{"given":"Krishna","family":"Chaitanya","sequence":"additional","affiliation":[]},{"given":"Pushpak","family":"Pati","sequence":"additional","affiliation":[]},{"given":"Chaitanya","family":"Parmar","sequence":"additional","affiliation":[]},{"given":"Pooya","family":"Mobadersany","sequence":"additional","affiliation":[]},{"given":"Shreyas","family":"Fadnavis","sequence":"additional","affiliation":[]},{"given":"Lindsey","family":"Surace","sequence":"additional","affiliation":[]},{"given":"Shadi","family":"Yarandi","sequence":"additional","affiliation":[]},{"given":"Louis R.","family":"Ghanem","sequence":"additional","affiliation":[]},{"given":"Molly","family":"Lucas","sequence":"additional","affiliation":[]},{"given":"Tommaso","family":"Mansi","sequence":"additional","affiliation":[]},{"given":"Gabriela Oana","family":"Cula","sequence":"additional","affiliation":[]},{"given":"Pablo F.","family":"Damasceno","sequence":"additional","affiliation":[]},{"given":"Kristopher","family":"Standish","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Allez, M., et\u00a0al.: A phase 2b, randomised, double-blind, placebo-controlled, parallel-arm, multicenter study evaluating the safety and efficacy of tesnatilimab in patients with moderately to severely active crohn\u2019s disease. Journal of Crohn\u2019s and Colitis p. jjad047 (2023)","DOI":"10.1093\/ecco-jcc\/jjad047"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Chaitanya, K., et al.: Arges: Spatio-temporal transformer for ulcerative colitis severity assessment in endoscopy videos. International Workshop on Machine Learning in Medical Imaging, MICCAI (2024)","DOI":"10.1007\/978-3-031-73290-4_20"},{"key":"1_CR3","unstructured":"Dermyer, P., Kalra, A., Schwartz, M.: Endodino: a foundation model for gi endoscopy. arXiv preprint arXiv:2501.05488 (2025)"},{"key":"1_CR4","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)"},{"key":"1_CR5","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"1_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1007\/978-3-030-32226-7_75","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"FC Ghesu","year":"2019","unstructured":"Ghesu, F.C., et al.: Quantifying and leveraging classification uncertainty for chest radiograph assessment. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 676\u2013684. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_75"},{"key":"1_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101855","volume":"68","author":"FC Ghesu","year":"2021","unstructured":"Ghesu, F.C., Georgescu, B., Mansoor, A., Yoo, Y., Gibson, E., Vishwanath, R., Balachandran, A., Balter, J.M., Cao, Y., Singh, R., et al.: Quantifying and leveraging predictive uncertainty for medical image assessment. Med. Image Anal. 68, 101855 (2021)","journal-title":"Med. Image Anal."},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Hirsch, R., et al.: Self-supervised learning for endoscopic video analysis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 569\u2013578. Springer (2023)","DOI":"10.1007\/978-3-031-43904-9_55"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Huang, L., Ruan, S., Decazes, P., Denoeux, T.: Evidential segmentation of 3d pet\/ct images. In: International conference on belief functions, pp. 159\u2013167. Springer (2021)","DOI":"10.1007\/978-3-030-88601-1_16"},{"key":"1_CR10","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"1_CR11","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 (2017)"},{"key":"1_CR12","unstructured":"Oquab, M., et\u00a0al.: Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Polat, G., Ergenc, I., Kani, H.T., Alahdab, Y.O., Atug, O., Temizel, A.: Class distance weighted cross-entropy loss for ulcerative colitis severity estimation. In: Annual Conference on Medical Image Understanding and Analysis, pp. 157\u2013171. Springer (2022)","DOI":"10.1007\/978-3-031-12053-4_12"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Rubin, D.T., et al.: Development of a novel ulcerative colitis endoscopic mayo score prediction model using machine learning. Gastro Hep Advances (2023)","DOI":"10.1016\/j.gastha.2023.06.003"},{"issue":"10342","key":"1_CR15","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1016\/S0140-6736(22)00688-2","volume":"399","author":"BE Sands","year":"2022","unstructured":"Sands, B.E., et al.: Ustekinumab versus adalimumab for induction and maintenance therapy in biologic-naive patients with moderately to severely active crohn\u2019s disease: a multicentre, randomised, double-blind, parallel-group, phase 3b trial. The Lancet 399(10342), 2200\u20132211 (2022)","journal-title":"The Lancet"},{"issue":"10","key":"1_CR16","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1093\/ecco-jcc\/jjy085","volume":"12","author":"BE Sands","year":"2018","unstructured":"Sands, B.E., et al.: Peficitinib, an oral janus kinase inhibitor, in moderate-to-severe ulcerative colitis: results from a randomised, phase 2 study. J. Crohns Colitis 12(10), 1158\u20131169 (2018)","journal-title":"J. Crohns Colitis"},{"issue":"13","key":"1_CR17","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1056\/NEJMoa1900750","volume":"381","author":"BE Sands","year":"2019","unstructured":"Sands, B.E., et al.: Ustekinumab as induction and maintenance therapy for ulcerative colitis. N. Engl. J. Med. 381(13), 1201\u20131214 (2019)","journal-title":"N. Engl. J. Med."},{"issue":"26","key":"1_CR18","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1056\/NEJM198712243172603","volume":"317","author":"KW Schroeder","year":"1987","unstructured":"Schroeder, K.W., Tremaine, W.J., Ilstrup, D.M.: Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. N. Engl. J. Med. 317(26), 1625\u20131629 (1987)","journal-title":"N. Engl. J. Med."},{"issue":"4","key":"1_CR19","first-page":"425","volume":"10","author":"E Schwab","year":"2022","unstructured":"Schwab, E., et al.: Automatic estimation of ulcerative colitis severity from endoscopy videos using ordinal multi-instance learning. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 10(4), 425\u2013433 (2022)","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization"},{"key":"1_CR20","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. Advances in neural information processing systems 31 (2018)"},{"issue":"1","key":"1_CR21","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1053\/j.gastro.2023.09.049","volume":"166","author":"RW Stidham","year":"2024","unstructured":"Stidham, R.W., et al.: Using computer vision to improve endoscopic disease quantification in therapeutic clinical trials of ulcerative colitis. Gastroenterology 166(1), 155\u2013167 (2024)","journal-title":"Gastroenterology"},{"issue":"5","key":"1_CR22","doi-asserted-by":"publisher","first-page":"e193963","DOI":"10.1001\/jamanetworkopen.2019.3963","volume":"2","author":"RW Stidham","year":"2019","unstructured":"Stidham, R.W., et al.: Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw. Open 2(5), e193963\u2013e193963 (2019)","journal-title":"JAMA Netw. Open"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Tian, Y., et al.: Contrastive transformer-based multiple instance learning for weakly supervised polyp frame detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 88\u201398. Springer (2022)","DOI":"10.1007\/978-3-031-16437-8_9"},{"key":"1_CR24","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s12530-018-9236-x","volume":"11","author":"MD Vasilakakis","year":"2020","unstructured":"Vasilakakis, M.D., Diamantis, D., Spyrou, E., Koulaouzidis, A., Iakovidis, D.K.: Weakly supervised multilabel classification for semantic interpretation of endoscopy video frames. Evol. Syst. 11, 409\u2013421 (2020)","journal-title":"Evol. Syst."},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, C., Zhang, S., Dou, Q.: Foundation model for endoscopy video analysis via large-scale self-supervised pre-train. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 101\u2013111. Springer (2023)","DOI":"10.1007\/978-3-031-43996-4_10"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Zou, K., Yuan, X., Shen, X., Wang, M., Fu, H.: Tbrats: trusted brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 503\u2013513. Springer (2022)","DOI":"10.1007\/978-3-031-16452-1_48"}],"container-title":["Lecture Notes in Computer Science","Uncertainty for Safe Utilization of Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06593-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T12:38:01Z","timestamp":1759063081000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06593-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"ISBN":["9783032065926","9783032065933"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06593-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"assertion":[{"value":"29 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"All authors were employees of Janssen R&D, LLC, when conducting this research and may own company stock\/stock options.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"UNSURE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unsure2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/unsuremiccai.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}