Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation

[2410.00046] Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Image and Video Processing arXiv:2410.00046 (eess) [Submitted on 27 Sep 2024 ( v1 ), last revised 28 Aug 2026 (this version, v4)] Title: Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation Authors: Yujin Oh , Sangjoon Park , Xiang Li , Pengfei Jin , Yi Wang , Jonathan Paly , Jason Efstathiou , Annie Chan , Jun Won Kim , Hwa Kyung Byun , Ik Jae Lee , Jaeho Cho , Chan Woo Wee , Peng Shu , Peilong Wang , Caiwen Jiang , Nathan Yu , Jason Holmes , Jong Chul Ye , Quanzheng Li , Wei Liu , Woong Sub Koom , Jin Sung Kim , Kyungsang Kim View PDF HTML (experimental) Abstract: Clinical decision-making reflects diverse strategies shaped by regional patient populations and institutional protocols. However, most existing medical artificial intelligence (AI) models are trained on highly prevalent data patterns, which reinforces biases and fails to capture the breadth of clinical expertise. Inspired by the recent advances in Mixture of Experts (MoE), we propose a Mixture of Multicenter Experts (MoME) framework to address AI bias in the medical domain without requiring data sharing across institutions. MoME integrates specialized expertise from diverse clinical strategies to enhance model generalizability and adaptability across medical centers. We validate this framework using a multimodal target volume delineation model for prostate cancer radiotherapy. With few-shot training that combines imaging and clinical notes from each center, the model outperformed baselines, particularly in settings with high inter-center variability or limited data availability. Furthermore, MoME enables model customization to local clinical preferences without cross-institutional data exchange, making it especially suitable for resource-constrained settings while promoting broadly generalizable medical AI. Comments: In Revission Subjects: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2410.00046 [eess.IV] (or arXiv:2410.00046v4 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2410.00046 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yujin Oh [ view email ] [v1] Fri, 27 Sep 2024 19:28:30 UTC (26,103 KB) [v2] Sat, 26 Oct 2024 15:22:04 UTC (11,322 KB) [v3] Thu, 18 Sep 2025 15:48:24 UTC (5,746 KB) [v4] Fri, 28 Aug 2026 03:52:15 UTC (6,898 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2024-10 Change to browse by: cs cs.CV cs.LG eess References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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