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A Multi-Agent Framework for Automated Machine Learning with Evidence-based Planning for Medical Image Analysis

Yiqing Shen, Mathias Unberath · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28410 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

深い学習は、オープンソースの動きによって推進された医学画像分析のデフォルトツールとなっています。しかし、特定のタスクで最高のパフォーマンスを達成することは困難であり、アーキテクチャの選択とハイパーパラメータ最適化(HPO)は依然として「科学よりも芸術」と見なされます。自動化された機械学習(AutoML)は、モデルトレーニングの部分を自動化するが、医学画像化ではほとんど役立ちません:一般的な目的のアプローチには医学のアーキテクチャとプレプロセッサが欠け、医学に焦点を当てたアプローチはエンジニアリングの専門知識を必要とし、3Dセグメントなどの狭いタスクに限定されています。我々は、

Machine translation (Meta M2M100); the English original follows.

Abstract

Deep learning has become the default tool for medical image analysis, driven by the open-source movement. However, achieving the best performance on a given task remains difficult, with architecture selection and hyperparameter optimization (HPO) still considered "more of an art than a science". Automated machine learning (AutoML) automates parts of model training, yet helps little in medical imaging: general-purpose approaches lack medical architectures and preprocessing, and medical-focused ones demand engineering expertise and remain confined to narrow tasks such as 3D segmentation. We introduce MedicalMLOmni, a multi-agent AutoML framework that automates model training for diverse single- and multi-modal medical tasks through a natural language interface. Specifically, MedicalMLOmni performs evidence-based planning by retrieving published results to guide model selection and configuration, and employs parallel HPO where multiple agents explore adjustment strategies concurrently. MedicalMLOmni achieves the best score on 19 of 20 benchmarks; PMC-VQA is the exception, where a vision-language model with broader pre-training leads. Backbone-matched controls attribute the gains to the framework, not the backbone. By removing coding requirements, MedicalMLOmni lowers the barrier for domain experts with rich datasets but limited engineering expertise.

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Topics: Computer science, Artificial intelligence, Machine learning, Task (project management), Hyperparameter, Domain (mathematical analysis)

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