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

arXiv:2410.00046 · eess.IV, cs.CV, cs.LG · Submitted 2024-09-27 · Read on arXiv

eess.IV, cs.CV, cs.LG

Submitted: 2024-09-27

Updated: 2026-08-28

Comments: In Revission

Code: https://github.com/tvseg/MoME-RO

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 50× in this paper · explained in 5 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Adaptability — 9× in this paper · explained in 1 episode
• Embeddings — 9× in this paper · explained in 2 episodes
• Generalizability — 9× in this paper · explained in 6 episodes
• Mixture of Multicenter Experts — 8× in this paper
• Planning Target Volume — 6× in this paper
• Sacrum-to-PTV Ratio — 5× in this paper
• Adaptation — 4× in this paper · explained in 1 episode
• Continual Learning — 4× in this paper · explained in 2 episodes
• Massachusetts General Hospital — 4× in this paper
• Artificial Intelligence (AI) — 3× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 5 episodes
• External Validation — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Mixture of Experts — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation Studies — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Federated Learning — 2× in this paper · explained in 14 episodes
• Gross Tumor Volume — 2× in this paper
• Harvard Medical School — 2× in this paper
• Loss Function — 2× in this paper · explained in 2 episodes
• Mixture of Experts (MoE) — 2× in this paper · explained in 5 episodes
• APEX — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• CANCER CLINICAL DATA FROM ELECTRONIC MEDICAL RECORDS — 1× in this paper
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Edge AI — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Institutional Review Boards — 1× in this paper
• Korea Advanced Institute of Science and Technology — 1× in this paper
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• MedVersa: A Generalist Foundation Model for Medical Image Interpretation
• Segment Anything Model (SAM) for Radiation Oncology
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• Divide and not forget: Ensemble of selectively trained experts in Continual Learning
• Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
• Segment Anything
• MONAI: An open-source framework for deep learning in healthcare
• ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax

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