mlsanityData to 14 Oct 2026

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

arXiv 2410.00046 ↗First posted 27 Sep 2024eess.IV cs.CV cs.LGcited by 1

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 18 of its 50 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

18 of 50 references.

References · 18

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2023Segment Anythingcited by 8,234
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  6. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  7. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  8. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  9. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
  10. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  11. 2023BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Taskscited by 201
  12. 2024Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapterscited by 199
6 more
  1. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
  2. 2024Divide and not forget: Ensemble of selectively trained experts in Continual Learningcited by 38
  3. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 29
  4. 2023Segment Anything Model (SAM) for Radiation Oncologycited by 24
  5. 2023LLM-driven Multimodal Target Volume Contouring in Radiation Oncologycited by 20
  6. 2023ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothoraxcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DuetMoE: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Analysiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology
  2. 2024M4oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Experts
  3. 2026Domain-Expert-Guided Hybrid Mixture-of-Experts for Medical AI: Integrating Data-Driven Learning with Clinical Priors
  4. 2025Medical AI Consensus: A Multi-Agent Framework for Radiology Report Generation and Evaluation
  5. 2025MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
  6. 2024Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy
  7. 2024Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging
  8. 2024Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models