{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T04:22:43Z","timestamp":1790396563954,"version":"4.1.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051813","type":"print"},{"value":"9783032051820","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"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\/978-3-032-05182-0_52","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:02Z","timestamp":1758153602000},"page":"533-542","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Regression-Assisted Classification for\u00a0CT-Based Portal Hypertension Diagnosis"],"prefix":"10.1007","author":[{"given":"Wuque","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongze","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezhong","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daqing","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"issue":"2","key":"52_CR1","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1586\/egh.12.83","volume":"7","author":"A Berzigotti","year":"2013","unstructured":"Berzigotti, A., Seijo, S., Reverter, E., Bosch, J.: Assessing portal hypertension in liver diseases. Expert Rev. Gastroenterol. Hepatol. 7(2), 141\u2013155 (2013)","journal-title":"Expert Rev. Gastroenterol. Hepatol."},{"issue":"10","key":"52_CR2","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1038\/nrgastro.2009.149","volume":"6","author":"J Bosch","year":"2009","unstructured":"Bosch, J., Abraldes, J.G., Berzigotti, A., Garc\u00eda-Pagan, J.C.: The clinical use of HVPG measurements in chronic liver disease. Nat. Rev. Gastroenterol. Hepatol. 6(10), 573\u2013582 (2009)","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"52_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103735","volume":"120","author":"A Bria","year":"2020","unstructured":"Bria, A., Marrocco, C., Tortorella, F.: Addressing class imbalance in deep learning for small lesion detection on medical images. Comput. Biol. Med. 120, 103735 (2020)","journal-title":"Comput. Biol. Med."},{"key":"52_CR4","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"52_CR5","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PmLR (2020)"},{"key":"52_CR6","doi-asserted-by":"publisher","first-page":"92857","DOI":"10.1109\/ACCESS.2023.3309732","volume":"11","author":"J Edward","year":"2023","unstructured":"Edward, J., Rosli, M.M., Seman, A.: A new multi-class rebalancing framework for imbalance medical data. IEEE Access 11, 92857\u201392874 (2023)","journal-title":"IEEE Access"},{"key":"52_CR7","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.cmpb.2016.10.007","volume":"138","author":"XW Gao","year":"2017","unstructured":"Gao, X.W., Hui, R., Tian, Z.: Classification of CT brain images based on deep learning networks. Comput. Methods Programs Biomed. 138, 49\u201356 (2017)","journal-title":"Comput. Methods Programs Biomed."},{"key":"52_CR8","unstructured":"Gildenblat, J.: Exploring Explainability for Vision Transformers (2020). https:\/\/jacobgil.github.io\/deeplearning\/vision-transformer-explainability"},{"key":"52_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"52_CR10","doi-asserted-by":"crossref","unstructured":"Huang, L., et al.: Serial quantitative chest CT assessment of COVID-19: a deep learning approach. Radiol. Cardiothoracic Imaging 2(2), e200075 (2020)","DOI":"10.1148\/ryct.2020200075"},{"key":"52_CR11","doi-asserted-by":"crossref","unstructured":"Jia, M., et al.: Visual prompt tuning. In: European Conference on Computer Vision, pp. 709\u2013727. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-19827-4_41"},{"issue":"3","key":"52_CR12","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1002\/hep.25752","volume":"56","author":"MY Kim","year":"2012","unstructured":"Kim, M.Y., et al.: Hepatic vein arrival time as assessed by contrast-enhanced ultrasonography is useful for the assessment of portal hypertension in compensated cirrhosis. Hepatology 56(3), 1053\u20131062 (2012)","journal-title":"Hepatology"},{"issue":"2","key":"52_CR13","first-page":"74","volume":"27","author":"A Kumar","year":"2008","unstructured":"Kumar, A., Sharma, P., Sarin, S.K., et al.: Hepatic venous pressure gradient measurement: time to learn. Indian J. Gastroenterol. 27(2), 74\u201380 (2008)","journal-title":"Indian J. Gastroenterol."},{"key":"52_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"52_CR15","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.ebiom.2018.09.023","volume":"36","author":"F Liu","year":"2018","unstructured":"Liu, F., et al.: Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study. EBioMedicine 36, 151\u2013158 (2018)","journal-title":"EBioMedicine"},{"issue":"13","key":"52_CR16","doi-asserted-by":"publisher","first-page":"2998","DOI":"10.1016\/j.cgh.2020.03.034","volume":"18","author":"Y Liu","year":"2020","unstructured":"Liu, Y., et al.: Deep convolutional neural network-aided detection of portal hypertension in patients with cirrhosis. Clin. Gastroenterol. Hepatol. 18(13), 2998\u20133007 (2020)","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"52_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"35","key":"52_CR18","doi-asserted-by":"publisher","first-page":"82107","DOI":"10.1007\/s11042-024-18578-1","volume":"83","author":"LP Maguluri","year":"2024","unstructured":"Maguluri, L.P., et al.: Adversarial deep learning for improved abdominal organ segmentation in CT scans. Multimed. Tools Appl. 83(35), 82107\u201382129 (2024)","journal-title":"Multimed. Tools Appl."},{"issue":"6","key":"52_CR19","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1016\/j.jhep.2016.07.021","volume":"65","author":"N Palaniyappan","year":"2016","unstructured":"Palaniyappan, N., et al.: Non-invasive assessment of portal hypertension using quantitative magnetic resonance imaging. J. Hepatol. 65(6), 1131\u20131139 (2016)","journal-title":"J. Hepatol."},{"key":"52_CR20","doi-asserted-by":"crossref","unstructured":"Ren, J., Zhang, M., Yu, C., Liu, Z.: Balanced MSE for imbalanced visual regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7926\u20137935 (2022)","DOI":"10.1109\/CVPR52688.2022.00777"},{"issue":"6","key":"52_CR21","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1053\/j.gastro.2008.03.007","volume":"134","author":"AJ Sanyal","year":"2008","unstructured":"Sanyal, A.J., Bosch, J., Blei, A., Arroyo, V.: Portal hypertension and its complications. Gastroenterology 134(6), 1715\u20131728 (2008)","journal-title":"Gastroenterology"},{"issue":"3","key":"52_CR22","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1148\/radiol.2018181131","volume":"289","author":"R Sartoris","year":"2018","unstructured":"Sartoris, R., et al.: Quantification of liver surface nodularity at CT: utility for detection of portal hypertension. Radiology 289(3), 698\u2013707 (2018)","journal-title":"Radiology"},{"key":"52_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104306","volume":"132","author":"S Serte","year":"2021","unstructured":"Serte, S., Demirel, H.: Deep learning for diagnosis of COVID-19 using 3D CT scans. Comput. Biol. Med. 132, 104306 (2021)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"52_CR24","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.221648","volume":"307","author":"C Wang","year":"2023","unstructured":"Wang, C., et al.: Diagnosis of clinically significant portal hypertension using CT-and MRI-based vascular model. Radiology 307(2), e221648 (2023)","journal-title":"Radiology"},{"key":"52_CR25","unstructured":"Yang, Y., Zha, K., Chen, Y., Wang, H., Katabi, D.: Delving into deep imbalanced regression. In: International Conference on Machine Learning, pp. 11842\u201311851. PMLR (2021)"},{"key":"52_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2024.100152","volume":"5","author":"P Zhao","year":"2024","unstructured":"Zhao, P., et al.: DiGAN breakthrough: advancing diabetic data analysis with innovative GAN-based imbalance correction techniques. Comput. Methods Programs Biomed. Update 5, 100152 (2024)","journal-title":"Comput. Methods Programs Biomed. Update"}],"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\/978-3-032-05182-0_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T08:29:45Z","timestamp":1776846585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05182-0_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,18]]},"ISBN":["9783032051813","9783032051820"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05182-0_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,18]]},"assertion":[{"value":"18 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":"23 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"}}]}}