{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T10:52:57Z","timestamp":1782125577377,"version":"3.54.5"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":52,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The high heterogeneity of cancer poses significant challenges for precision diagnosis, particularly in tasks such as rare subtype identification, early lesion detection, and tumor grading. Notably, cancer cell biological traits are closely correlated with cell death regulatory mechanisms, and accurate cancer region identification is a pivotal premise for exploring the cancer-cell death intrinsic association. Single-modality methods often struggle to balance sensitivity and accuracy, while existing general multimodal models are poorly adapted to the processing of pathological long texts and high-resolution images, leading to issues of semantic truncation and feature loss. To address these challenges, this study proposes CanLRHI, a multimodal pretraining model tailored for cancer pathology that focuses on the synergistic modeling of long pathological reports and high-resolution images to achieve comprehensive cross-modal alignment and accurate characterization of cancer regions. Experimental results on the CancerPath-170\u00a0K-v1 dataset, which contains 170 000 cancer pathology image\u2013text pairs, demonstrate that CanLRHI significantly outperforms mainstream multimodal baselines across various tasks, including Zero-Shot classification and Few-Shot Fine-Tuning. This work provides an extensible technical framework for long-text-driven cross-modal representation learning in medical pathology, and further offers a reliable technical support for cell death-related cancer pathology research via high-precision cancer region detection.<\/jats:p>","DOI":"10.1093\/bib\/bbag341","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:49:48Z","timestamp":1780487388000},"source":"Crossref","is-referenced-by-count":0,"title":["CanLRHI: a multimodal pretraining model for cell death analysis in cancer pathology based on long-text representation and high-resolution images"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9807-8620","authenticated-orcid":false,"given":"Tianjiao","family":"Zhang","sequence":"first","affiliation":[{"name":"The School of Computer Science and Artificial Intelligence, Northeast Forestry University , 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3411-0895","authenticated-orcid":false,"given":"Long","family":"Wan","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Artificial Intelligence, Northeast Forestry University , 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Artificial Intelligence, Northeast Forestry University , 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6754-7109","authenticated-orcid":false,"given":"Zhongqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Artificial Intelligence, Northeast Forestry University , 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijie","family":"Cui","sequence":"additional","affiliation":[{"name":"The Department of Radiation Oncology, Harbin Medical University Cancer Hospital , 150 Haping Road, Nangang District, Harbin, Heilongjiang 150081 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianli","family":"Ma","sequence":"additional","affiliation":[{"name":"The Department of Radiation Oncology, Harbin Medical University Cancer Hospital , 150 Haping Road, Nangang District, Harbin, Heilongjiang 150081 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"2026062206410130600_ref1","first-page":"772","article-title":"Heterogeneity and cancer","volume":"28","author":"Allison","year":"2014","journal-title":"Oncology"},{"key":"2026062206410130600_ref2","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: challenges and opportunities","volume":"33","author":"Madabhushi","year":"2016","journal-title":"Med Image Anal"},{"key":"2026062206410130600_ref3","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/978-1-4614-3223-4_14","volume-title":"Mining Text Data","author":"Simpson","year":"2012"},{"key":"2026062206410130600_ref4","first-page":"8748","volume-title":"International Conference on Machine Learning","author":"Radford","year":"2021"},{"key":"2026062206410130600_ref5","first-page":"9694","article-title":"Align before fuse: vision and language representation learning with momentum distillation","volume":"34","author":"Li","year":"2021","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2026062206410130600_ref6","first-page":"12888","volume-title":"International Conference on Machine Learning","author":"Li","year":"2022"},{"key":"2026062206410130600_ref7","doi-asserted-by":"crossref","first-page":"3876","DOI":"10.18653\/v1\/2022.emnlp-main.256","volume-title":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing","author":"Wang","year":"2022"},{"key":"2026062206410130600_ref8","doi-asserted-by":"crossref","first-page":"7871","DOI":"10.18653\/v1\/2020.acl-main.703","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Lewis","year":"2020"},{"key":"2026062206410130600_ref9","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/978-3-031-72983-6_18","volume-title":"European Conference on Computer Vision","author":"Zhang","year":"2024"},{"key":"2026062206410130600_ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3293318","article-title":"A survey of zero-shot learning: settings, methods, and applications","volume":"10","author":"Wang","year":"2019","journal-title":"ACM Trans Intell Syst Technol"},{"key":"2026062206410130600_ref11","first-page":"4582","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Xian","year":"2017"},{"key":"2026062206410130600_ref12","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.52202\/068431-0142","article-title":"Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning","volume":"35","author":"Liu","year":"2022","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2026062206410130600_ref13","first-page":"2425","volume-title":"Proceedings of the IEEE international conference on computer vision","author":"Antol","year":"2015"},{"key":"2026062206410130600_ref14","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1109\/JPROC.2024.3525147","article-title":"Cross-modal retrieval: a systematic review of methods and future directions","volume":"112","author":"Wang","year":"2025","journal-title":"Proc IEEE"},{"key":"2026062206410130600_ref15","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-031-43993-3_51","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Lin","year":"2023"},{"key":"2026062206410130600_ref16","doi-asserted-by":"publisher","first-page":"267D","DOI":"10.1093\/nar\/gkh061","article-title":"The unified medical language system (UMLS): integrating biomedical terminology","volume":"32","author":"Bodenreider","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2026062206410130600_ref17","doi-asserted-by":"publisher","first-page":"g5878","DOI":"10.1136\/bmj.g5878","article-title":"Standardising outcome measures using z scores","volume":"349","author":"Sedgwick","year":"2014","journal-title":"BMJ"},{"key":"2026062206410130600_ref18","journal-title":"International Conference on Learning Representations, 2021"},{"key":"2026062206410130600_ref19","first-page":"1597","volume-title":"International Conference on Machine Learning","author":"Chen","year":"2020"},{"key":"2026062206410130600_ref20","first-page":"5200","volume-title":"International Conference on Machine Learning","author":"Gower","year":"2019"},{"key":"2026062206410130600_ref21","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1109\/TBME.2014.2325410","article-title":"Brain tumor segmentation based on local independent projection-based classification","volume":"61","author":"Huang","year":"2014","journal-title":"IEEE Trans Biomed Eng"},{"key":"2026062206410130600_ref22","doi-asserted-by":"publisher","volume-title":"Mendeley Data","author":"Syed","DOI":"10.17632\/5kbjrgsncf.1"},{"key":"2026062206410130600_ref23","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1109\/TITB.2010.2050695","article-title":"Automatic classification of lymphoma images with transform-based global features","volume":"14","author":"Orlov","year":"2010","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"2026062206410130600_ref24","doi-asserted-by":"publisher","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"Data in brief"},{"key":"2026062206410130600_ref25","first-page":"1","volume-title":"2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)","author":"Hossain","year":"2019"},{"key":"2026062206410130600_ref26","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","article-title":"A visual-language foundation model for computational pathology","volume":"30","author":"Lu","year":"2024","journal-title":"Nat Med"},{"key":"2026062206410130600_ref27","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1038\/s41591-023-02504-3","article-title":"A visual\u2013language foundation model for pathology image analysis using medical twitter","volume":"29","author":"Huang","year":"2023","journal-title":"Nat Med"},{"key":"2026062206410130600_ref28","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2026062206410130600_ref29","first-page":"1126","volume-title":"International Conference on Machine Learning","author":"Finn","year":"2017"},{"key":"2026062206410130600_ref30","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"nature"},{"key":"2026062206410130600_ref31","author":"Nickparvar","year":"2026"},{"key":"2026062206410130600_ref32","doi-asserted-by":"crossref","first-page":"7346","DOI":"10.18653\/v1\/2024.emnlp-main.418","volume-title":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing","author":"Chen","year":"2024"},{"key":"2026062206410130600_ref33","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-031-73033-7_4","volume-title":"European Conference on Computer Vision","author":"Sun","year":"2024"},{"key":"2026062206410130600_ref34","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1038\/sj.cdd.4402148","article-title":"Cell death modalities: classification and pathophysiological implications","volume":"14","author":"Galluzzi","year":"2007","journal-title":"Cell Death & Differentiation,"},{"key":"2026062206410130600_ref35","doi-asserted-by":"crossref","first-page":"41076","DOI":"10.52202\/075280-1788","article-title":"Visual instruction tuning","volume":"36","author":"Liu","year":"2023","journal-title":"Advances in neural information processing systems, Advances in neural information processing systems"},{"key":"2026062206410130600_ref36","first-page":"353","volume-title":"Machine Learning for Health (ML4H)","author":"Moor","year":"2023"},{"key":"2026062206410130600_ref37","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1158\/2159-8290.CD-20-0789","article-title":"Modes of regulated cell death in cancer","volume":"11","author":"Koren","year":"2021","journal-title":"Cancer Discov"},{"key":"2026062206410130600_ref38","doi-asserted-by":"publisher","first-page":"634690","DOI":"10.3389\/fcell.2021.634690","article-title":"Guidelines for regulated cell death assays: a systematic summary, a categorical comparison, a prospective","volume":"9","author":"Hu","year":"2021","journal-title":"Front Cell Dev Biol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/3\/bbag341\/68570200\/bbag341.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/3\/bbag341\/68570200\/bbag341.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T10:41:12Z","timestamp":1782124872000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbag341\/8713050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbag341","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,5]]},"published":{"date-parts":[[2026,5]]},"article-number":"bbag341"}}