An open-source ecosystem has made deep learning the default choice for medical image analysis. Yet selecting the right architecture and tuning its hyperparameters remains a stubborn bottleneck, often requiring repeated manual experimentation. A new preprint on arXiv tackles this by framing automated machine learning as a multi-agent planning problem.
The framework introduces specialised agents that collaborate on evidence-based planning: rather than running random or exhaustive searches, they record outcomes from previous runs and use that information to guide subsequent choices. This mirrors how a human practitioner would learn from past trials, but in a structured and scalable way.
Because the abstract is truncated, details on evaluation and comparisons to existing AutoML methods are not available. Still, the core idea is a step toward making high-performance deep learning more accessible for medical imaging tasks, where robust results are especially critical.