{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T07:46:54Z","timestamp":1785829614994,"version":"3.56.0"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Anhui Provincial Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.media.2026.104213","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:43:51Z","timestamp":1783698231000},"page":"104213","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Adapting pathology foundation models for continual cross-center WSI retrieval"],"prefix":"10.1016","volume":"113","author":[{"given":"Xinyu","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiguo","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2945-7795","authenticated-orcid":false,"given":"Jun","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3302-0481","authenticated-orcid":false,"given":"Yushan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.media.2026.104213_b1","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgh-2022-008763","article-title":"Enabling research capacity strengthening within a consortium context: a qualitative study","volume":"7","author":"Aiyenigba","year":"2022","journal-title":"BMJ Glob. Health"},{"key":"10.1016\/j.media.2026.104213_b2","series-title":"European Conference on Computer Vision","first-page":"692","article-title":"Transfer without forgetting","author":"Boschini","year":"2022"},{"key":"10.1016\/j.media.2026.104213_b3","unstructured":"Caccia, L., Aljundi, R., Asadi, N., Tuytelaars, T., Pineau, J., Belilovsky, E., 2022. New Insights on Reducing Abrupt Representation Change in Online Continual Learning. In: The Tenth International Conference on Learning Representations. ICLR."},{"key":"10.1016\/j.media.2026.104213_b4","unstructured":"Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M., 2019. Efficient Lifelong Learning with A-GEM. In: 7th International Conference on Learning Representations. ICLR."},{"key":"10.1016\/j.media.2026.104213_b5","doi-asserted-by":"crossref","unstructured":"Chen, R.J., Chen, C., Li, Y., Chen, T.Y., Trister, A.D., Krishnan, R.G., Mahmood, F., 2022a. Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 16144\u201316155.","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"10.1016\/j.media.2026.104213_b6","doi-asserted-by":"crossref","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a general-purpose foundation model for computational pathology","author":"Chen","year":"2024","journal-title":"Nature Med."},{"key":"10.1016\/j.media.2026.104213_b7","series-title":"Learning Meaningful Representations of Life, NeurIPS 2021","article-title":"Self-supervised vision transformers learn visual concepts in histopathology","author":"Chen","year":"2021"},{"key":"10.1016\/j.media.2026.104213_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ccell.2022.07.004","article-title":"Pan-cancer integrative histology-genomic analysis via multimodal deep learning","author":"Chen","year":"2022","journal-title":"Cancer Cell"},{"issue":"12","key":"10.1016\/j.media.2026.104213_b9","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1038\/s41551-022-00929-8","article-title":"Fast and scalable search of whole-slide images via self-supervised deep learning","volume":"6","author":"Chen","year":"2022","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.media.2026.104213_b10","doi-asserted-by":"crossref","unstructured":"Davari, M., Asadi, N., Mudur, S., Aljundi, R., Belilovsky, E., 2022. Probing representation forgetting in supervised and unsupervised continual learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16712\u201316721.","DOI":"10.1109\/CVPR52688.2022.01621"},{"issue":"7","key":"10.1016\/j.media.2026.104213_b11","first-page":"3366","article-title":"A continual learning survey: Defying forgetting in classification tasks","volume":"44","author":"De Lange","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2026.104213_b12","first-page":"1","article-title":"A multimodal whole-slide foundation model for pathology","author":"Ding","year":"2025","journal-title":"Nature Med."},{"key":"10.1016\/j.media.2026.104213_b13","series-title":"Tutorial on variational autoencoders","author":"Doersch","year":"2016"},{"key":"10.1016\/j.media.2026.104213_b14","series-title":"ICLR","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.media.2026.104213_b15","doi-asserted-by":"crossref","unstructured":"Fini, E., Da Costa, V.G.T., Alameda-Pineda, X., Ricci, E., Alahari, K., Mairal, J., 2022. Self-supervised models are continual learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9621\u20139630.","DOI":"10.1109\/CVPR52688.2022.00940"},{"key":"10.1016\/j.media.2026.104213_b16","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1245\/s10434-016-5624-2","article-title":"Establishing a cancer research consortium in low-and middle-income countries: challenges faced and lessons learned","volume":"24","author":"Fischer","year":"2017","journal-title":"Ann. Surg. Oncol."},{"key":"10.1016\/j.media.2026.104213_b17","doi-asserted-by":"crossref","unstructured":"He, J., Duan, Z., Zhu, F., 2025. CL-LoRA: Continual low-rank adaptation for rehearsal-free class-incremental learning. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 30534\u201330544.","DOI":"10.1109\/CVPR52734.2025.02843"},{"key":"10.1016\/j.media.2026.104213_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103163","article-title":"Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval","volume":"95","author":"Hu","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104213_b19","first-page":"1","article-title":"A visual\u2013language foundation model for pathology image analysis using medical Twitter","author":"Huang","year":"2023","journal-title":"Nature Med."},{"key":"10.1016\/j.media.2026.104213_b20","doi-asserted-by":"crossref","first-page":"79963","DOI":"10.52202\/079017-2541","article-title":"Free lunch in pathology foundation model: Task-specific model adaptation with concept-guided feature enhancement","volume":"37","author":"Huang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2026.104213_b21","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhao, W., Wang, S., Fu, Y., Jiang, Y., Yu, L., 2023. ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 21349\u201321360.","DOI":"10.1109\/ICCV51070.2023.01952"},{"key":"10.1016\/j.media.2026.104213_b22","doi-asserted-by":"crossref","unstructured":"Jung, D., Han, D., Bang, J., Song, H., 2023. Generating Instance-level Prompts for Rehearsal-free Continual Learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 11847\u201311857.","DOI":"10.1109\/ICCV51070.2023.01088"},{"key":"10.1016\/j.media.2026.104213_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101757","article-title":"Yottixel\u2013an image search engine for large archives of histopathology whole slide images","volume":"65","author":"Kalra","year":"2020","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.media.2026.104213_b24","doi-asserted-by":"crossref","first-page":"11008","DOI":"10.1109\/TPAMI.2023.3269810","article-title":"High-order correlation-guided slide-level histology retrieval with self-supervised hashing","volume":"45","author":"Li","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2026.104213_b25","article-title":"Gradient episodic memory for continual learning","volume":"30","author":"Lopez-Paz","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"6","key":"10.1016\/j.media.2026.104213_b26","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-efficient and weakly supervised computational pathology on whole-slide images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.media.2026.104213_b27","series-title":"Conference on Lifelong Learning Agents","first-page":"60","article-title":"Continual learning with foundation models: An empirical study of latent replay","author":"Ostapenko","year":"2022"},{"key":"10.1016\/j.media.2026.104213_b28","series-title":"Variational Methods for Machine Learning with Applications To Deep Networks","first-page":"111","article-title":"Variational autoencoder","author":"Pinheiro Cinelli","year":"2021"},{"key":"10.1016\/j.media.2026.104213_b29","series-title":"Prism: A multi-modal generative foundation model for slide-level histopathology","author":"Shaikovski","year":"2024"},{"key":"10.1016\/j.media.2026.104213_b30","doi-asserted-by":"crossref","unstructured":"Smith, J.S., Karlinsky, L., Gutta, V., Cascante-Bonilla, P., Kim, D., Arbelle, A., Panda, R., Feris, R., Kira, Z., 2023. CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 11909\u201311919.","DOI":"10.1109\/CVPR52729.2023.01146"},{"issue":"2","key":"10.1016\/j.media.2026.104213_b31","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgh-2018-001318","article-title":"Managing health research capacity strengthening consortia: a systematised review of the published literature","volume":"4","author":"Tagoe","year":"2019","journal-title":"BMJ Glob. Health"},{"key":"10.1016\/j.media.2026.104213_b32","article-title":"Self-supervised representation distribution learning for reliable data augmentation in histopathology WSI classification","author":"Tang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2026.104213_b33","doi-asserted-by":"crossref","first-page":"68","DOI":"10.5114\/wo.2014.47136","article-title":"Review the cancer genome atlas (TCGA): an immeasurable source of knowledge","volume":"2015","author":"Tomczak","year":"2015","journal-title":"Contemp. Oncol.\/Wsp\u00f3\u0142czesna Onkologia"},{"issue":"1","key":"10.1016\/j.media.2026.104213_b34","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/3147.3165","article-title":"Random sampling with a reservoir","volume":"11","author":"Vitter","year":"1985","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"key":"10.1016\/j.media.2026.104213_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102645","article-title":"RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval","volume":"83","author":"Wang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104213_b36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, Z., Hong, X., 2022. S-Prompts Learning with Pre-trained Transformers: An Occam\u2019s Razor for Domain Incremental Learning. In: Conference on Neural Information Processing Systems. NeurIPS.","DOI":"10.52202\/068431-0411"},{"key":"10.1016\/j.media.2026.104213_b37","article-title":"Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality","author":"Wang","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2026.104213_b38","article-title":"DualPrompt: Complementary prompting for rehearsal-free continual learning","author":"Wang","year":"2022","journal-title":"Eur. Conf. Comput. Vis."},{"key":"10.1016\/j.media.2026.104213_b39","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T., 2022b. Learning to prompt for continual learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 139\u2013149.","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"10.1016\/j.media.2026.104213_b40","article-title":"A comprehensive survey of continual learning: theory, method and application","author":"Wang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8035","key":"10.1016\/j.media.2026.104213_b41","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1038\/s41586-024-07894-z","article-title":"A pathology foundation model for cancer diagnosis and prognosis prediction","volume":"634","author":"Wang","year":"2024","journal-title":"Nature"},{"key":"10.1016\/j.media.2026.104213_b42","doi-asserted-by":"crossref","DOI":"10.1038\/s41586-024-07441-w","article-title":"A whole-slide foundation model for digital pathology from real-world data","author":"Xu","year":"2024","journal-title":"Nature"},{"key":"10.1016\/j.media.2026.104213_b43","series-title":"Proceedings of the 32nd ACM International Conference on Multimedia","first-page":"8913","article-title":"Generating prompts in latent space for rehearsal-free continual learning","author":"Yang","year":"2024"},{"key":"10.1016\/j.media.2026.104213_b44","doi-asserted-by":"crossref","unstructured":"Zang, Y., Huang, C., Loy, C.C., 2021. Fasa: Feature augmentation and sampling adaptation for long-tailed instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 3457\u20133466.","DOI":"10.1109\/ICCV48922.2021.00344"},{"key":"10.1016\/j.media.2026.104213_b45","article-title":"Diagnostic regions attention network (DRA-Net) for histopathology WSI recommendation and retrieval","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104213_b46","article-title":"Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval","author":"Zheng","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104213_b47","series-title":"Medical Image Computing and Computer-Assisted Intervention","first-page":"459","article-title":"Tracing diagnosis paths on histopathology WSIs for diagnostically relevant case recommendation","author":"Zheng","year":"2020"},{"issue":"9","key":"10.1016\/j.media.2026.104213_b48","doi-asserted-by":"crossref","first-page":"2726","DOI":"10.1109\/TMI.2023.3264781","article-title":"Kernel attention transformer for histopathology whole slide image analysis and assistant cancer diagnosis","volume":"42","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104213_b49","first-page":"1","article-title":"Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need","author":"Zhou","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.media.2026.104213_b50","series-title":"Proceedings of Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","article-title":"Lifelong histopathology whole slide image retrieval via distance consistency rehearsal","volume":"Vol. LNCS 15004","author":"Zhu","year":"2024"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526002823?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526002823?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T07:04:00Z","timestamp":1785827040000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526002823"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":50,"alternative-id":["S1361841526002823"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.104213","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Adapting pathology foundation models for continual cross-center WSI retrieval","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.104213","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104213"}}