{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:46:25Z","timestamp":1758267985196,"version":"3.44.0"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049773","type":"print"},{"value":"9783032049780","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-04978-0_47","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T16:17:36Z","timestamp":1758212256000},"page":"491-501","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PATE: Enhancing Few-Shot Pathological Image Classification via\u00a0Prompt-Based Text-Image Embedding Adaptation"],"prefix":"10.1007","author":[{"given":"Shenghao","family":"Chen","sequence":"first","affiliation":[]},{"given":"Zhen","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xiaoqian","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Han","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chunjiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shaohua Kevin","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"47_CR1","doi-asserted-by":"crossref","unstructured":"Alt, C., H\u00fcbner, M., Hennig, L.: Fine-tuning pre-trained transformer language models to distantly supervised relation extraction. arXiv preprint arXiv:1906.08646 (2019)","DOI":"10.18653\/v1\/P19-1134"},{"issue":"12","key":"47_CR2","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1136\/bmjqs-2020-012456","volume":"30","author":"CF Branson","year":"2021","unstructured":"Branson, C.F., et al.: Improving diagnostic performance through feedback: the diagnosis learning cycle. BMJ Qual. Saf. 30(12), 1002\u20131009 (2021)","journal-title":"BMJ Qual. Saf."},{"key":"47_CR3","doi-asserted-by":"crossref","unstructured":"Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S.: Large scale fine-grained categorization and domain-specific transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4109\u20134118 (2018)","DOI":"10.1109\/CVPR.2018.00432"},{"key":"47_CR4","doi-asserted-by":"publisher","first-page":"264","DOI":"10.3389\/fmed.2019.00264","volume":"6","author":"N Dimitriou","year":"2019","unstructured":"Dimitriou, N., Arandjelovi\u0107, O., Caie, P.D.: Deep learning for whole slide image analysis: an overview. Front. Med. 6, 264 (2019)","journal-title":"Front. Med."},{"key":"47_CR5","unstructured":"Ding, C., et al.: LOBG: less overfitting for better generalization in vision-language model. arXiv preprint arXiv:2410.10247 (2024)"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Du, Y., Liu, Z., Li, J., Zhao, W.X.: A survey of vision-language pre-trained models. arXiv preprint arXiv:2202.10936 (2022)","DOI":"10.24963\/ijcai.2022\/762"},{"issue":"3","key":"47_CR7","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s00428-023-03518-5","volume":"482","author":"C Eloy","year":"2023","unstructured":"Eloy, C., et al.: Artificial intelligence-assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies. Virchows Arch. 482(3), 595\u2013604 (2023)","journal-title":"Virchows Arch."},{"issue":"2","key":"47_CR8","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s11263-023-01891-x","volume":"132","author":"P Gao","year":"2024","unstructured":"Gao, P., et al.: CLIP-adapter: better vision-language models with feature adapters. Int. J. Comput. Vision 132(2), 581\u2013595 (2024)","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"47_CR9","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/s43586-021-00018-1","volume":"1","author":"M Hafner","year":"2021","unstructured":"Hafner, M., et al.: CLIP and complementary methods. Nat. Rev. Methods Primers 1(1), 20 (2021)","journal-title":"Nat. Rev. Methods Primers"},{"key":"47_CR10","unstructured":"Han, Z., Gao, C., Liu, J., Zhang, J., Zhang, S.Q.: Parameter-efficient fine-tuning for large models: a comprehensive survey. arXiv preprint arXiv:2403.14608 (2024)"},{"key":"47_CR11","doi-asserted-by":"crossref","unstructured":"Harb, R., Pock, T., M\u00fcller, H.: Diffusion-based generation of histopathological whole slide images at a gigapixel scale. In: WACV, pp. 5131\u20135140 (2024)","DOI":"10.1109\/WACV57701.2024.00505"},{"issue":"5","key":"47_CR12","first-page":"939","volume":"19","author":"Z Huang","year":"2024","unstructured":"Huang, Z., et al.: Pele scores: pelvic x-ray landmark detection with pelvis extraction and enhancement. IJCARS 19(5), 939\u2013950 (2024)","journal-title":"IJCARS"},{"key":"47_CR13","doi-asserted-by":"crossref","unstructured":"Huang, Z., et al.: CASEmark: a hybrid model for robust anatomical landmark detection in multi-structure x-rays. J. King Saud Univ. Comput. Inf. Sci. 37(3), 1\u201318 (2025)","DOI":"10.1007\/s44443-025-00031-4"},{"key":"47_CR14","doi-asserted-by":"publisher","unstructured":"Jia, M., Tang, L., Chen, B.C., Cardie, C., Belongie, S., Hariharan, B., Lim, S.N.: Visual prompt tuning. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13693, pp. 709\u2013727. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19827-4_41","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"47_CR15","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/PL00009240","volume":"22","author":"DE Knuth","year":"1998","unstructured":"Knuth, D.E.: Linear probing and graphs. Algorithmica 22, 561\u2013568 (1998)","journal-title":"Algorithmica"},{"issue":"6","key":"47_CR16","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1136\/amiajnl-2012-001540","volume":"20","author":"S Kothari","year":"2013","unstructured":"Kothari, S., Phan, J.H., Stokes, T.H., Wang, M.D.: Pathology imaging informatics for quantitative analysis of whole-slide images. J. Am. Med. Inform. Assoc. 20(6), 1099\u20131108 (2013)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"1","key":"47_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1017\/pan.2023.20","volume":"32","author":"M Laurer","year":"2024","unstructured":"Laurer, M., Van Atteveldt, W., Casas, A., Welbers, K.: Less annotating, more classifying: Addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert-nli. Polit. Anal. 32(1), 84\u2013100 (2024)","journal-title":"Polit. Anal."},{"key":"47_CR18","unstructured":"Li, X., et\u00a0al.: Artificial general intelligence for medical imaging analysis. IEEE Rev. Biomed. Eng. (2024)"},{"key":"47_CR19","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"47_CR20","unstructured":"Nouyed, M.I.: Efficient classification of very high resolution images (2024)"},{"issue":"4","key":"47_CR21","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1162\/COLI_a_00232","volume":"41","author":"E Prud\u2019hommeaux","year":"2015","unstructured":"Prud\u2019hommeaux, E., Roark, B.: Graph-based word alignment for clinical language evaluation. Comput. Linguist. 41(4), 549\u2013578 (2015)","journal-title":"Comput. Linguist."},{"key":"47_CR22","unstructured":"Qu, L., Fu, K., Wang, M., Song, Z., et al.: The rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification. In: NIPS, vol. 36, pp. 67551\u201367564 (2023)"},{"key":"47_CR23","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: ICML, pp. 8748\u20138763. PMLR (2021)"},{"issue":"6","key":"47_CR24","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","volume":"34","author":"D Ramachandram","year":"2017","unstructured":"Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. 34(6), 96\u2013108 (2017)","journal-title":"IEEE Signal Process. Mag."},{"key":"47_CR25","doi-asserted-by":"crossref","unstructured":"Rawhani, M., Karabo\u011fa, D., Nalbantoglu, U., Ba\u015ft\u00fcrk, A., Akay, B.: Efficient unsupervised domain adaptation with PEFT combinations. In: 2024 9th International Conference on Computer Science and Engineering (UBMK), pp. 169\u2013174. IEEE (2024)","DOI":"10.1109\/UBMK63289.2024.10773425"},{"key":"47_CR26","doi-asserted-by":"publisher","first-page":"128019","DOI":"10.1016\/j.energy.2023.128019","volume":"279","author":"Z Ren","year":"2023","unstructured":"Ren, Z., Han, H., Cui, X., Lu, H., Luo, M.: Novel data-pulling-based strategy for chiller fault diagnosis in data-scarce scenarios. Energy 279, 128019 (2023)","journal-title":"Energy"},{"key":"47_CR27","unstructured":"Shao, S., Yuan, X., Huang, Z., Qiu, Z., Wang, S., Zhou, K.: DiffuseExpand: expanding dataset for 2D medical image segmentation using diffusion models. arXiv preprint arXiv:2304.13416 (2023)"},{"key":"47_CR28","unstructured":"Shen, S., et al.: How much can clip benefit vision-and-language tasks? arXiv preprint arXiv:2107.06383 (2021)"},{"key":"47_CR29","unstructured":"Tsuneki, M., Kanavati, F.: Inference of captions from histopathological patches. In: International Conference on Medical Imaging with Deep Learning, pp. 1235\u20131250. PMLR (2022)"},{"key":"47_CR30","doi-asserted-by":"crossref","unstructured":"Ul\u00a0Haq, M.U., Rigoni, D., Sperduti, A.: Prompt-based data augmentation using contrastive learning under scarcity of annotated data. In: ECAI 2024, pp. 2717\u20132724. IOS Press (2024)","DOI":"10.3233\/FAIA240805"},{"key":"47_CR31","unstructured":"Wang, R., et al.: ECAMP: entity-centered context-aware medical vision language pre-training. arXiv preprint arXiv:2312.13316 (2023)"},{"key":"47_CR32","unstructured":"Wang, Z., Liang, J., He, R., Xu, N., Wang, Z., Tan, T.: Improving zero-shot generalization for clip with synthesized prompts. arXiv preprint arXiv:2307.07397 (2023)"},{"key":"47_CR33","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: Text-guided foundation model adaptation for pathological image classification. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14224, pp. 272\u2013282. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43904-9_27","DOI":"10.1007\/978-3-031-43904-9_27"},{"key":"47_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, A., et al.: Domain-adaptive few-shot learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1390\u20131399 (2021)","DOI":"10.1109\/WACV48630.2021.00143"},{"key":"47_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: CVPR, pp. 16816\u201316825 (2022)","DOI":"10.1109\/CVPR52688.2022.01631"},{"issue":"9","key":"47_CR36","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. IJCV 130(9), 2337\u20132348 (2022)","journal-title":"IJCV"},{"issue":"5","key":"47_CR37","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","volume":"109","author":"SK Zhou","year":"2021","unstructured":"Zhou, S.K., et al.: A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc. IEEE 109(5), 820\u2013838 (2021)","journal-title":"Proc. IEEE"},{"key":"47_CR38","unstructured":"Zhou, X., Huang, Z., Zhu, H., Yao, Q., Zhou, S.K.: Hybrid attention network: an efficient approach for anatomy-free landmark detection. arXiv preprint arXiv:2412.06499 (2024)"}],"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\/978-3-032-04978-0_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:05:08Z","timestamp":1758233108000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04978-0_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049773","9783032049780"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04978-0_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 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"}}]}}