用AI代理自动构建医学影像模型基线，省时高效。
Can Coding Agents Build Robust Baselines? A Skill-Based Approach for Automating the Medical Imaging Model-Development Pipeline
- 基于文献引导的智能体流程，自动完成代码生成与实验设计。
- 在4个公开数据集上均取得竞争力结果，最高获6名（15队）。
- 无需针对任务调整，适合快速搭建可靠基线的研究者。
医学影像深度学习基线的开发仍是一个高度迭代的过程，需查阅文献、实现代码、实验调优并经专家精炼。现有自动化方法多仅优化单一环节，如架构搜索或超参数调优，而非完整开发流程。本文提出一种智能体式AI科学家工作流，结合文献引导推理、自动化代码生成和假设驱动实验，用于生成具有竞争力的医学影像基线模型。该框架在四个涵盖分割、分类与检测的公开基准上进行评估。所有任务中，实验流水线均持续提升验证性能，获得有竞争力的排行榜成绩：在PUMA两个赛道分别位列第6名（共15支队伍），MILK10k中获第31名（共125支队伍）。在MIDOG25上，所生成模型还展现出跨扫描仪、肿瘤类型及物种的强泛化能力。使用同一工作流处理所有挑战而无需任务特异性重构，证明了基于技能、文献引导的智能体工作流可显著降低开发竞争性医学影像基线所需工程成本。
原文摘要 · Abstract (English)
Developing competitive deep learning baselines for medical imaging remains a highly iterative process requiring literature review, implementation, experimentation, and expert refinement. Existing automation approaches typically optimize isolated components, such as architecture search or hyperparameter tuning, rather than the complete baseline development process. We present an agentic AI Scientist workflow that combines literature-guided reasoning, automated code generation, and hypothesis-driven experimentation to generate competitive baseline models for medical imaging challenges. The framework is evaluated on four public benchmarks spanning segmentation, classification, and detection. Across all tasks, the Experimentation Pipeline consistently improves validation performance, achieving competitive leaderboard results, including 6th place on both PUMA tracks (15 teams) and 31st place on MILK10k (125 teams). On MIDOG25, the resulting model also demonstrates strong domain generalization across scanners, tumor types, and species. Using the same workflow across all challenges without task-specific redesign, we demonstrate that skill-based, literature-guided agentic workflows can substantially reduce the engineering effort required to develop competitive medical imaging baselines.
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