{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:28:13Z","timestamp":1757618893389,"version":"3.44.0"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031986901"},{"type":"electronic","value":"9783031986918"}],"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_14","type":"book-chapter","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T06:09:39Z","timestamp":1752473379000},"page":"190-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Out-of-Distribution Detection in\u00a0Gastrointestinal Vision by\u00a0Estimating Nearest Centroid Distance Deficit"],"prefix":"10.1007","author":[{"given":"Sandesh","family":"Pokhrel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay","family":"Bhandari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharib","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tryphon","family":"Lambrou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yash Raj","family":"Shrestha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angus","family":"Watson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prashnna","family":"Gyawali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binod","family":"Bhattarai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Almeida, S.D., et\u00a0al.: cOOpD: reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023 (2023)","DOI":"10.1007\/978-3-031-43904-9_4"},{"key":"14_CR2","unstructured":"Ammar, M.B., et\u00a0al.: NECO: NEural collapse based out-of-distribution detection. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Chan, R., et\u00a0al.: Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5128\u20135137 (2021)","DOI":"10.1109\/ICCV48922.2021.00508"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Chheda, T., et\u00a0al.: Gastrointestinal tract anomaly detection from endoscopic videos using object detection approach. In: Advances in Visual Computing. Springer (2020)","DOI":"10.1007\/978-3-030-64559-5_39"},{"key":"14_CR5","unstructured":"Das, R., et\u00a0al.: On the separability of classes with the cross-entropy loss function. arXiv preprint arXiv:1909.06930 (2019)"},{"key":"14_CR6","unstructured":"Dinari, O., et\u00a0al.: Variational-and metric-based deep latent space for out-of-distribution detection. In: The 38th Conference on Uncertainty in Artificial Intelligence (2022)"},{"key":"14_CR7","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Haile, M.B., et\u00a0al.: Detection and classification of gastrointestinal disease using convolutional neural network and SVM. Cogent Eng. (2022)","DOI":"10.1080\/23311916.2022.2084878"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"He, K., et\u00a0al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR10","unstructured":"Hendrycks, D., et\u00a0al.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: International Conference on Learning Representations (2017)"},{"key":"14_CR11","unstructured":"Hendrycks, D., et\u00a0al.: Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations (2019)"},{"key":"14_CR12","unstructured":"Hendrycks, D., et\u00a0al.: Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132 (2019)"},{"issue":"4","key":"14_CR13","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s10120-018-0793-2","volume":"21","author":"T Hirasawa","year":"2018","unstructured":"Hirasawa, T., et al.: Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21(4), 653\u2013660 (2018). https:\/\/doi.org\/10.1007\/s10120-018-0793-2","journal-title":"Gastric Cancer"},{"key":"14_CR14","unstructured":"Jeleni\u0107, F., et\u00a0al.: Out-of-Distribution detection by leveraging between-layer transformation smoothness. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Jha, D., et\u00a0al.: GastroVision: a multi-class endoscopy image dataset for computer aided gastrointestinal disease detection. In: Workshop on Machine Learning for Multimodal Healthcare Data, pp. 125\u2013140. Springer (2023)","DOI":"10.1007\/978-3-031-47679-2_10"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Koulaouzidis, A., et\u00a0al.: How should we do colon capsule endoscopy reading: a practical guide. Therap. Adv. Gastrointest. Endosc. (2021)","DOI":"10.1177\/26317745211001983"},{"key":"14_CR17","unstructured":"Liang, S., et\u00a0al.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: International Conference on Learning Representations (2018)"},{"key":"14_CR18","unstructured":"Liu, L., et\u00a0al.: Fast decision boundary based out-of-distribution detector. In: Forty-first International Conference on Machine Learning (2024)"},{"key":"14_CR19","unstructured":"Liu, W., et\u00a0al.: Energy-based out-of-distribution detection. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 21464\u201321475 (2020)"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Mehta, D., et\u00a0al.: Out-of-distribution detection for long-tailed and fine-grained skin lesion images. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022. Springer (2022)","DOI":"10.1007\/978-3-031-16431-6_69"},{"key":"14_CR21","unstructured":"Mishra, D., et\u00a0al.: Dual conditioned diffusion models for out-of-distribution detection: application to fetal ultrasound videos. In: Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023. Springer (2023)"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Oukdach, Y., et\u00a0al.: Gastrointestinal diseases classification based on deep learning and transfer learning mechanism. In: 2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) (2022)","DOI":"10.1109\/WINCOM55661.2022.9966474"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Pogorelov, K., et\u00a0al.: KVASIR: a multi-class image dataset for computer aided gastrointestinal disease detection. In: Proceedings of the 8th ACM on Multimedia Systems Conference, pp. 164\u2013169 (2017)","DOI":"10.1145\/3083187.3083212"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Pokhrel, S., et\u00a0al.: TTA-OOD: test-time augmentation for improving out-of-distribution detection in gastrointestinal vision. In: MICCAI Workshop on Data Engineering in Medical Imaging. Springer (2024)","DOI":"10.1007\/978-3-031-73748-0_4"},{"key":"14_CR25","doi-asserted-by":"publisher","first-page":"102606","DOI":"10.1016\/j.artmed.2023.102606","volume":"143","author":"A Quind\u00f3s","year":"2023","unstructured":"Quind\u00f3s, A., et al.: Self-supervised out-of-distribution detection in wireless capsule endoscopy images. Artif. Intell. Med. 143, 102606 (2023)","journal-title":"Artif. Intell. Med."},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Sharma, A., et\u00a0al.: Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images. Int. J. Med. Inform. (2023)","DOI":"10.1016\/j.ijmedinf.2023.105142"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Sharmila, V., et\u00a0al.: Detection and classification of GI-tract anomalies from endoscopic images using deep learning. In: 2022 IEEE 19th India Council International Conference (INDICON) (2022)","DOI":"10.1109\/INDICON56171.2022.10039766"},{"key":"14_CR28","unstructured":"Sinhamahapatra, P., et\u00a0al.: Is it all a cluster game?\u2013exploring out-of-distribution detection based on clustering in the embedding space. arXiv preprint arXiv:2203.08549 (2022)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Sivari, E., et\u00a0al.: A new approach for gastrointestinal tract findings detection and classification: deep learning-based hybrid stacking ensemble models. Diagnostics (2023)","DOI":"10.3390\/diagnostics13040720"},{"key":"14_CR30","unstructured":"Sun, Y., et\u00a0al.: Out-of-distribution detection with deep nearest neighbors. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research. PMLR (2022)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Sundar, V.K., et\u00a0al.: Out-of-distribution detection in multi-label datasets using latent space of $$\\beta $$-VAE. In: 2020 IEEE Security and Privacy Workshops (SPW). IEEE (2020)","DOI":"10.1109\/SPW50608.2020.00057"},{"key":"14_CR32","unstructured":"Tolstikhin, I.O., et\u00a0al.: MLP-mixer: an all-MLP architecture for vision. In: Advances in Neural Information Processing Systems (2021)"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Touvron, H., et\u00a0al.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning. PMLR (2021)","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"14_CR34","doi-asserted-by":"crossref","unstructured":"Wang, R., et\u00a0al.: Global, regional, and national burden of 10 digestive diseases in 204 countries and territories from 1990 to 2019. Front. Public Health (2023)","DOI":"10.3389\/fpubh.2023.1061453"},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Y., et\u00a0al.: Global burden of digestive diseases: a systematic analysis of the global burden of diseases study, 1990 to 2019. Gastroenterology (2023)","DOI":"10.1016\/S0016-5085(23)01635-9"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Zadorozhny, K., Thoral, P., Elbers, P., Cin\u00e0, G.: Out-of-distribution detection for medical applications: guidelines for practical evaluation. In: Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence, pp. 137\u2013153. Springer (2022)","DOI":"10.1007\/978-3-031-14771-5_10"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et\u00a0al.: Decoupling MaxLogit for out-of-distribution detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.00330"},{"key":"14_CR38","doi-asserted-by":"crossref","unstructured":"Zimmerer, D., et\u00a0al.: MOOD 2020: a public benchmark for out-of-distribution detection and localization on medical images. IEEE Trans. Med. Imaging (2022)","DOI":"10.1109\/TMI.2022.3170077"}],"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_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T08:04:23Z","timestamp":1757232263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-98691-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"ISBN":["9783031986901","9783031986918"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-98691-8_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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"}}]}}