Towards Fully Automated Medical Imaging Code Generation via Validation-based Context Engineering

2026-08-28

AI Summary

为解决复杂医疗图像处理任务中自动化代码生成问题，提出AutoMedImg框架，通过多阶段验证和自动上下文工程实现无需人工干预的代码生成。

Abstract

Large language models (LLMs) have demonstrated considerable promise in program generation for small-scale and conventional application development; however, they remain limited when applied to complex, domain-specific tasks such as medical image processing. General-purpose models lack explicit domain knowledge and robust validation mechanisms to ensure correctness, often requiring substantial human intervention to produce reliable processing pipelines. To address these limitations, we propose AutoMedImg, a multi-agent framework for fully automated medical image processing code generation. AutoMedImg orchestrates specialised agents across two phases: a Planning Phase that performs dataset analysis and architecture design with semantic and formal verification, and a Coding Phase that generates modules in parallel with static checking, execution testing, and assembly validation. This multi-stage validation mitigates error propagation throughout generation, while comprehensive auto-context engineering combining domain-specific knowledge bases, shared memory, and validation feedback automates context construction without manual prompting. A cross-project adaptive pipeline synthesis mechanism further accumulates validated pipelines and retrieves proven components for new tasks based on project similarity, enhancing generation efficiency through cross-project learning. Extensive evaluation across six diverse and well-established medical imaging datasets with five backbone LLMs demonstrates that AutoMedImg achieves zero human intervention, with Dice scores of up to 0.90 for segmentation tasks and 99% accuracy for classification.

Problem

• Large language models
• Medical image processing
• Domain-specific knowledge
• Validation mechanisms

Innovation

• multi-agent framework
• automated medical image processing
• multi-stage validation
• auto-context engineering
• cross-project adaptive pipeline synthesis