{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:45:35Z","timestamp":1777268735589,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Suzhou Gusu talent plan for Health Technical Personnel project","award":["GSWS2021024"],"award-info":[{"award-number":["GSWS2021024"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["BK20250383"],"award-info":[{"award-number":["BK20250383"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Nanjing Medical University Gusu School Youth Talent Development Program","award":["Grant No. GSKY20250523"],"award-info":[{"award-number":["Grant No. GSKY20250523"]}]},{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["SJCX25_1793"],"award-info":[{"award-number":["SJCX25_1793"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02423-w","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T13:33:13Z","timestamp":1771248793000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging radiology and pathology: domain-generalized cross-modal learning for clinical"],"prefix":"10.1038","volume":"9","author":[{"given":"Xiang","family":"Zhong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuo","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manimurugan","family":"Shanmuganathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingming","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqin","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"2423_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.177","volume":"4","author":"RS Lee","year":"2017","unstructured":"Lee, R. S. et al. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4, 1\u20139 (2017).","journal-title":"Sci. Data"},{"key":"2423_CR2","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","volume":"19","author":"IC Moreira","year":"2012","unstructured":"Moreira, I. C. et al. Inbreast: toward a full-field digital mammographic database. Academic Radiol. 19, 236\u2013248 (2012).","journal-title":"Academic Radiol."},{"key":"2423_CR3","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.media.2019.05.010","volume":"56","author":"G Aresta","year":"2019","unstructured":"Aresta, G. et al. Bach: Grand challenge on breast cancer histology images. Med. image Anal. 56, 122\u2013139 (2019).","journal-title":"Med. image Anal."},{"key":"2423_CR4","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giy065","volume":"7","author":"G Litjens","year":"2018","unstructured":"Litjens, G. et al. 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset. GigaScience 7, giy065 (2018).","journal-title":"GigaScience"},{"key":"2423_CR5","doi-asserted-by":"publisher","first-page":"e241805","DOI":"10.1148\/radiol.241805","volume":"315","author":"Y Huang","year":"2025","unstructured":"Huang, Y. et al. Nomogram for predicting neoadjuvant chemotherapy response in breast cancer using mri-based intratumoral heterogeneity quantification. Radiology 315, e241805 (2025).","journal-title":"Radiology"},{"key":"2423_CR6","doi-asserted-by":"publisher","first-page":"112086","DOI":"10.1016\/j.ejrad.2025.112086","volume":"187","author":"F Schwarzhans","year":"2025","unstructured":"Schwarzhans, F. et al. Image normalization techniques and their effect on the robustness and predictive power of breast MRI radiomics. Eur. J. Radiol. 187, 112086 (2025).","journal-title":"Eur. J. Radiol."},{"key":"2423_CR7","doi-asserted-by":"publisher","first-page":"4410","DOI":"10.1158\/1078-0432.CCR-21-4148","volume":"28","author":"N Braman","year":"2022","unstructured":"Braman, N. et al. Novel radiomic measurements of tumor-associated vasculature morphology on clinical imaging as a biomarker of treatment response in multiple cancers. Clin. Cancer Res. 28, 4410\u20134424 (2022).","journal-title":"Clin. Cancer Res."},{"key":"2423_CR8","doi-asserted-by":"crossref","unstructured":"Shubeitah, M., Hasasneh, A. & Albarqouni, S. Two-steps approach for breast cancer detection and classification using convolutional neural networks. Int. J. Eng. Appl. 12, (2024).","DOI":"10.15866\/irea.v12i6.24446"},{"key":"2423_CR9","unstructured":"Wei, X. et al. Vikl: A mammography interpretation framework via multimodal aggregation of visual-knowledge-linguistic features. arXiv preprint arXiv:2409.15744 (2024)."},{"key":"2423_CR10","unstructured":"Hou, J. et al. Self-explainable ai for medical image analysis: A survey and new outlooks. arXiv preprint arXiv:2410.02331 (2024)."},{"key":"2423_CR11","doi-asserted-by":"crossref","unstructured":"Wang, A. Q. et al. A framework for interpretability in machine learning for medical imaging. IEEE Access 12, 53277\u201353292 (2024)..","DOI":"10.1109\/ACCESS.2024.3387702"},{"key":"2423_CR12","doi-asserted-by":"crossref","unstructured":"Musa, A., Prasad, R. & Hernandez, M. Addressing cross-population domain shift in chest x-ray classification through supervised adversarial domain adaptation. Sci. Rep. 15, 11383 (2025)..","DOI":"10.1038\/s41598-025-95390-3"},{"key":"2423_CR13","unstructured":"Sethi, S. et al. ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning. In Proc. of the 10th Machine Learning for Healthcare Conference (eds Agrawal, M. et al) Vol. 298 https:\/\/proceedings.mlr.press\/v298\/sethi25a.html (PMLR, 2025)."},{"key":"2423_CR14","unstructured":"Mayilvahanan, P. et al. In Search of Forgotten DomainGeneralization. International Conference on Learning Representations (ICLR), (Spotlight) (2025)."},{"key":"2423_CR15","doi-asserted-by":"crossref","unstructured":"Tian, Y. et al. Learning vision from models rivals learning vision from data. In Proc. of the IEEE\/CVF conference on computer vision and pattern recognition, 15887\u201315898 (2024).","DOI":"10.1109\/CVPR52733.2024.01504"},{"key":"2423_CR16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, Y. & Zhang, H. Lost domain generalization is a natural consequence of lack of training domains. In Proc. of the AAAI Conference on Artificial Intelligence Vol. 38, 15689\u201315697 (2024).","DOI":"10.1609\/aaai.v38i14.29497"},{"key":"2423_CR17","doi-asserted-by":"crossref","unstructured":"Tan, Z., Yang, X. & Huang, K. Rethinking multi-domain generalization with a general learning objective. In Proc. of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 23512\u201323522 (2024).","DOI":"10.1109\/CVPR52733.2024.02219"},{"key":"2423_CR18","doi-asserted-by":"crossref","unstructured":"Addepalli, S., Asokan, A. R., Sharma, L. & Babu, R. V. Leveraging vision-language models for improving domain generalization in image classification. In Proc. of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 23922\u201323932 (2024).","DOI":"10.1109\/CVPR52733.2024.02258"},{"key":"2423_CR19","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1007\/s11263-023-01913-8","volume":"132","author":"K Zhou","year":"2024","unstructured":"Zhou, K., Yang, Y., Qiao, Y. & Xiang, T. Mixstyle neural networks for domain generalization and adaptation. Int. J. Computer Vis. 132, 822\u2013836 (2024).","journal-title":"Int. J. Computer Vis."},{"key":"2423_CR20","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10462-024-10922-z","volume":"57","author":"AG Khoee","year":"2024","unstructured":"Khoee, A. G., Yu, Y. & Feldt, R. Domain generalization through meta-learning: a survey. Artif. Intell. Rev. 57, 285 (2024).","journal-title":"Artif. Intell. Rev."},{"key":"2423_CR21","doi-asserted-by":"crossref","unstructured":"Bai, S. et al. Diprompt: Disentangled prompt tuning for multiple latent domain generalization in federated learning. In Proc. of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 27284\u201327293 (2024).","DOI":"10.1109\/CVPR52733.2024.02576"},{"key":"2423_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y. et al. Federated domain generalization: A survey. In Proc. of the IEEE (IEEE, 2025).","DOI":"10.1109\/JPROC.2025.3596173"},{"key":"2423_CR23","doi-asserted-by":"crossref","unstructured":"Yan, S. et al. Prompt-Driven Latent Domain Generalization for Medical Image Classification. IEEE Transac. Med. Imaging 44, 348\u2013360 (2025).","DOI":"10.1109\/TMI.2024.3443119"},{"key":"2423_CR24","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1038\/s41591-024-02915-w","volume":"30","author":"F Tian","year":"2024","unstructured":"Tian, F. et al. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning. Nat. Med. 30, 1309\u20131319 (2024).","journal-title":"Nat. Med."},{"key":"2423_CR25","doi-asserted-by":"publisher","first-page":"100086","DOI":"10.59717\/j.xinn-life.2024.100086","volume":"2","author":"H Li","year":"2024","unstructured":"Li, H., Wang, S., Zhang, Y. & Li, W. A new paradigm for cytology-based artificial intelligence-assisted prediction for cancers of unknown primary origins. Innov. Life 2, 100086 (2024).","journal-title":"Innov. Life"},{"key":"2423_CR26","doi-asserted-by":"crossref","unstructured":"Ghani, H. et al. Gpsai: A clinically validated ai tool for tissue of origin prediction during routine tumor profiling. Cancer Res. Commun. 5, 1477\u20131489 (2025).","DOI":"10.1158\/2767-9764.CRC-25-0171"},{"key":"2423_CR27","doi-asserted-by":"crossref","unstructured":"Xin, H. et al. Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic ct data: a retrospective, multicentre study. EClin. Med. 69, (2024).","DOI":"10.1016\/j.eclinm.2024.102464"},{"key":"2423_CR28","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1038\/s41586-024-07894-z","volume":"634","author":"X Wang","year":"2024","unstructured":"Wang, X. et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634, 970\u2013978 (2024).","journal-title":"Nature"},{"key":"2423_CR29","doi-asserted-by":"publisher","first-page":"bbae028","DOI":"10.1093\/bib\/bbae028","volume":"25","author":"W Ma","year":"2024","unstructured":"Ma, W. et al. New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges. Brief. Bioinforma. 25, bbae028 (2024).","journal-title":"Brief. Bioinforma."},{"key":"2423_CR30","doi-asserted-by":"crossref","unstructured":"Wang, H. et al. Clap: learning transferable binary code representations with natural language supervision. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, 503\u2013515 (2024).","DOI":"10.1145\/3650212.3652145"},{"key":"2423_CR31","doi-asserted-by":"publisher","first-page":"5625","DOI":"10.1109\/TPAMI.2024.3369699","volume":"46","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Huang, J., Jin, S. & Lu, S. Vision-language models for vision tasks: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 46, 5625\u20135644 (2024).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2423_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Y. et al. Exploring the transferability of visual prompting for multimodal large language models. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 26562\u201326572 (2024).","DOI":"10.1109\/CVPR52733.2024.02508"},{"key":"2423_CR33","unstructured":"Rezaei, R. et al. Learning visual prompts for guiding the attention of vision transformers. In Proc. of TV4 Workshop (ICLR, 2025)."},{"key":"2423_CR34","doi-asserted-by":"crossref","unstructured":"Ndir, T. C., Schirrmeister, R. T. & Ball, T. EEG-CLIP: Learning EEG representations from natural language descriptions. Front. Robot. AI 12, 1625731 (2025).","DOI":"10.3389\/frobt.2025.1625731"},{"key":"2423_CR35","first-page":"122952","volume":"37","author":"K Yuan","year":"2024","unstructured":"Yuan, K., Navab, N., Padoy, N. et al. Procedure-aware surgical video-language pretraining with hierarchical knowledge augmentation. Adv. Neural Inf. Process. Syst. 37, 122952\u2013122983 (2024).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2423_CR36","unstructured":"Jiang, X. et al. Supervised fine-tuning in turn improves visual foundation models. arXiv preprint arXiv:2401.10222 (2024)."},{"key":"2423_CR37","doi-asserted-by":"publisher","first-page":"110250","DOI":"10.1016\/j.patcog.2024.110250","volume":"149","author":"F Zheng","year":"2024","unstructured":"Zheng, F. et al. Exploring low-resource medical image classification with weakly supervised prompt learning. Pattern Recognit. 149, 110250 (2024).","journal-title":"Pattern Recognit."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02423-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02423-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02423-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:12:22Z","timestamp":1777266742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02423-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,16]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2423"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02423-w","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,16]]},"assertion":[{"value":"10 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"251"}}