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

Key points

• Deep learning has become the default tool for medical image analysis, driven by the open-source movement.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]A Multi-Agent Framework for Automated Machine Learning with Evidence-based Planning for Medical Image Analysis
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 03:49 PM

  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.

  Deep learning has become the default tool for medical image analysis, driven by the open-source movement.

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