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http://hdl.handle.net/1893/37938| Appears in Collections: | Faculty of Health Sciences and Sport Journal Articles |
| Peer Review Status: | Refereed |
| Title: | Foundation model embeddings for multimodal oncology data integration |
| Author(s): | Menon, Tara P Mahajan, Arjun Powell, Dylan |
| Contact Email: | dylan.powell@stir.ac.uk |
| Issue Date: | 10-Jan-2026 |
| Date Deposited: | 24-Mar-2026 |
| Citation: | Menon TP, Mahajan A & Powell D (2026) Foundation model embeddings for multimodal oncology data integration. <i>npj Digital Medicine</i>, 9 (1), Art. No.: 131. https://doi.org/10.1038/s41746-025-02312-8 |
| Abstract: | Cancer care generates vast quantities of data including clinical records, pathology images, radiology scans, and molecular profiles, yet these modalities are rarely integrated in a systematic, automated manner within routine clinical workflows, remaining largely siloed across separate departmental and technical systems. Foundation model-driven embeddings—or numerical representations (vectors) that summarize complex data such as text, images ,and molecular profiles—offer a framework to integrate these data streams into unified patient representations. Here we examine the HONeYBEE platform’s approach to multimodal integration in oncology, situate it within broader developments in representation learning, and clinical and technical challenges that may.shape its path to implementation |
| DOI Link: | 10.1038/s41746-025-02312-8 |
| Rights: | This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License,which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by nc-nd/4.0/. |
| Licence URL(s): | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| Foundation model embeddings for multimodal oncology data integration.pdf | Fulltext - Published Version | 1.84 MB | Adobe PDF | View/Open |
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