Auto-nnU-Net自动优化医学图像分割模型，提升精度且控制计算资源消耗。
Auto-nnU-Net: Towards Automated Medical Image Segmentation
- 通过超参优化与神经架构搜索，全自动配置分割模型。
- 在10个数据集上6个表现显著提升，其余持平且资源开销可控。
- 适合需要高效部署的医疗影像分析场景，尤其资源受限环境。
医学图像分割任务多样，从骨骼到器官分割均面临模型选择难题。现有最先进的nnU-Net框架虽自动化程度高，但仍受限于固定超参数和启发式设计。本文提出Auto-nnU-Net，作为全自动化机器学习框架，实现超参数优化（HPO）、神经架构搜索（NAS）及分层架构搜索（HNAS）。我们还引入正则化先验带（Regularized PriorBand），在模型精度与训练资源间取得平衡，缓解真实医疗环境中训练资源有限的问题。在医学分割十项全能（Medical Segmentation Decathlon）的多个数据集上评估，结果表明该方法在10个数据集中的6个上显著提升nnU-Net性能，其余保持相当，同时满足实际应用的资源约束。代码已开源。
原文摘要 · Abstract (English)
Medical Image Segmentation (MIS) includes diverse tasks, from bone to organ segmentation, each with its own challenges in finding the best segmentation model. The state-of-the-art AutoML-related MIS-framework nnU-Net automates many aspects of model configuration but remains constrained by fixed hyperparameters and heuristic design choices. As a full-AutoML framework for MIS, we propose Auto-nnU-Net, a novel nnU-Net variant enabling hyperparameter optimization (HPO), neural architecture search (NAS), and hierarchical NAS (HNAS). Additionally, we propose Regularized PriorBand to balance model accuracy with the computational resources required for training, addressing the resource constraints often faced in real-world medical settings that limit the feasibility of extensive training procedures. We evaluate our approach across diverse MIS datasets from the well-established Medical Segmentation Decathlon, analyzing the impact of AutoML techniques on segmentation performance, computational efficiency, and model design choices. The results demonstrate that our AutoML approach substantially improves the segmentation performance of nnU-Net on 6 out of 10 datasets and is on par on the other datasets while maintaining practical resource requirements. Our code is available at https://github.com/automl/AutoNNUnet.
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