Mixture of Multicenter Experts in Multimodal Generative AI for Advanced Radiotherapy Target Delineation
<|reference_start|>Mixture of Multicenter Experts in Multimodal Generative AI for Advanced Radiotherapy Target Delineation: Clinical experts employ diverse philosophies and strategies in patient care, influenced by regional patient populations. However, existing medical artificial intelligence (AI) models are often trained on data distributions that disproportionately reflect highly prevalent patterns, reinforcing biases and overlooking the diverse expertise of clinicians. To overcome this limitation, we introduce the Mixture of Multicenter Experts (MoME) approach. This method strategically integrates specialized expertise from diverse clinical strategies, enhancing the AI model's ability to generalize and adapt across multiple medical centers. The MoME-based multimodal target volume delineation model, trained with few-shot samples including images and clinical notes from each medical center, outperformed baseline methods in prostate cancer radiotherapy target delineation. The advantages of MoME were most pronounced when data characteristics varied across centers or when data availability was limited, demonstrating its potential for broader clinical applications.Therefore, the MoME framework enables the deployment of AI-based target volume delineation models in resource-constrained medical facilities by adapting to specific preferences of each medical center only using a few sample data, without the need for data sharing between institutions. Expanding the number of multicenter experts within the MoME framework will significantly enhance the generalizability, while also improving the usability and adaptability of clinical AI applications in the field of precision radiation oncology.<|reference_end|>
@article{oh2024mixture,
    title={Mixture of Multicenter Experts in Multimodal Generative AI for Advanced
  Radiotherapy Target Delineation},
    author={Yujin Oh, Sangjoon Park, Xiang Li, Wang Yi, Jonathan Paly, Jason
  Efstathiou, Annie Chan, Jun Won Kim, Hwa Kyung Byun, Ik Jae Lee, Jaeho Cho,
  Chan Woo Wee, Peng Shu, Peilong Wang, Nathan Yu, Jason Holmes, Jong Chul Ye,
  Quanzheng Li, Wei Liu, Woong Sub Koom, Jin Sung Kim, Kyungsang Kim},
    journal={arXiv preprint arXiv:2410.00046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00046},
    primaryClass={eess.IV cs.CV cs.LG}
}