{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T04:46:02Z","timestamp":1758343562056,"version":"3.44.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051684","type":"print"},{"value":"9783032051691","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-05169-1_2","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:49:28Z","timestamp":1758318568000},"page":"14-23","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Contrastive Ordinal Regression Method for\u00a0Advancing Disease Severity Assessment in\u00a0Imbalanced Medical Datasets"],"prefix":"10.1007","author":[{"given":"Afsah","family":"Saleem","sequence":"first","affiliation":[]},{"given":"Joshua R.","family":"Lewis","sequence":"additional","affiliation":[]},{"given":"Syed Zulqarnain","family":"Gilani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)","journal-title":"Data Brief"},{"key":"2_CR2","unstructured":"AS: A-hybrid-contrastive-ordinal-regression (2025). https:\/\/github.com\/AfsahS\/A-Hybrid-Contrastive-Ordinal-Regression"},{"key":"2_CR3","unstructured":"Beckham, C., Pal, C.: Unimodal probability distributions for deep ordinal classification. In: International Conference on Machine Learning, pp. 411\u2013419. PMLR (2017)"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Dai, W., Li, X., Chiu, W.H.K., Kuo, M.D., Cheng, K.T.: Adaptive contrast for image regression in computer-aided disease assessment. IEEE Trans. Med. Imaging (2021)","DOI":"10.1109\/TMI.2021.3137854"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Diaz, R., Marathe, A.: Soft labels for ordinal regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4738\u20134747 (2019)","DOI":"10.1109\/CVPR.2019.00487"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., et al.: Scalp-supervised contrastive learning for cardiopulmonary disease classification and localization in chest x-rays using patient metadata. In: 2021 IEEE International Conference on Data Mining (ICDM) (2021)","DOI":"10.1109\/ICDM51629.2021.00134"},{"key":"2_CR7","unstructured":"Kaggle: Diabetic retinopathy detection. https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection. kaggle Competition (2015)"},{"key":"2_CR8","unstructured":"Khosla, P., et al.: Supervised contrastive learning. Adv. Neural Inf. Process. Syst. (2020)"},{"key":"2_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110748","volume":"156","author":"Y Lei","year":"2024","unstructured":"Lei, Y., Li, Z., Li, Y., Zhang, J., Shan, H.: Core: learning consistent ordinal representations with convex optimization for image ordinal estimation. Pattern Recogn. 156, 110748 (2024)","journal-title":"Pattern Recogn."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Lei, Y., Zhu, H., Zhang, J., Shan, H.: Meta ordinal regression forest for medical image classification with ordinal labels. IEEE\/CAA J. Automatica Sinica (2022)","DOI":"10.1109\/JAS.2022.105668"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Li, W., Huang, X., Lu, J., Feng, J., Zhou, J.: Learning probabilistic ordinal embeddings for uncertainty-aware regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13896\u201313905 (2021)","DOI":"10.1109\/CVPR46437.2021.01368"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Liu, X., Zou, Y., Song, Y., Yang, C., You, J., K\u00a0Vijaya\u00a0Kumar, B.: Ordinal regression with neuron stick-breaking for medical diagnosis. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11024-6_23"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output cnn for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920\u20134928 (2016)","DOI":"10.1109\/CVPR.2016.532"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Saleem, A., et al.: Scol: supervised contrastive ordinal loss for abdominal aortic calcification scoring on vertebral fracture assessment scans. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2023)","DOI":"10.1007\/978-3-031-43987-2_27"},{"issue":"1","key":"2_CR15","doi-asserted-by":"publisher","first-page":"12495","DOI":"10.1038\/s41598-019-48995-4","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 12495 (2019)","journal-title":"Sci. Rep."},{"key":"2_CR16","unstructured":"Van\u00a0der Vaart, A.W.: Asymptotic Statistics, vol.\u00a03. Cambridge university press, Cambridge (2000)"},{"key":"2_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108310","volume":"122","author":"VM Vargas","year":"2022","unstructured":"Vargas, V.M., Guti\u00e9rrez, P.A., Herv\u00e1s-Mart\u00ednez, C.: Unimodal regularisation based on beta distribution for deep ordinal regression. Pattern Recogn. 122, 108310 (2022)","journal-title":"Pattern Recogn."},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Wang, J., Cheng, Y., Chen, J., Chen, T., Chen, D., Wu, J.: Ord2seq: regarding ordinal regression as label sequence prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5865\u20135875 (2023)","DOI":"10.1109\/ICCV51070.2023.00539"},{"key":"2_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102559","volume":"81","author":"X Wang","year":"2022","unstructured":"Wang, X., et al.: Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022)","journal-title":"Med. Image Anal."},{"key":"2_CR20","unstructured":"Zhang, S., Yang, L., Mi, M.B., Zheng, X., Yao, A.: Improving deep regression with ordinal entropy. arXiv preprint arXiv:2301.08915 (2023)"},{"issue":"8","key":"2_CR21","doi-asserted-by":"publisher","first-page":"4084","DOI":"10.1109\/TNNLS.2021.3055816","volume":"33","author":"H Zhu","year":"2021","unstructured":"Zhu, H., et al.: Convolutional ordinal regression forest for image ordinal estimation. IEEE Trans. Neural Netw. Learn. Syst. 33(8), 4084\u20134095 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05169-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:49:35Z","timestamp":1758318575000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05169-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051684","9783032051691"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05169-1_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","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":"23 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":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}