Neural Architecture Search (NAS), a framework which automates the task of
designing neural networks, has recently been actively studied in the field of
deep learning. However, there are only a few NAS methods suitable for 3D
medical image segmentation. Medical 3D images are generally very large; thus it
is difficult to apply previous NAS methods due to their GPU computational
burden and long training time. We propose the resource-optimized neural
architecture search method which can be applied to 3D medical segmentation
tasks in a short training time (1.39 days for 1GB dataset) using a small amount
of computation power (one RTX 2080Ti, 10.8GB GPU memory). Excellent performance
can also be achieved without retraining(fine-tuning) which is essential in most
NAS methods. These advantages can be achieved by using a reinforcement
learning-based controller with parameter sharing and focusing on the optimal
search space configuration of macro search rather than micro search. Our
experiments demonstrate that the proposed NAS method outperforms manually
designed networks with state-of-the-art performance in 3D medical image
segmentation.

本研究提出一种基于强化学习的小样本、低计算力的 NAS 方法，优化了宏搜索空间配置，可用于大规模的 3D 医学图像分割，无需微调、训练时间仅为 1.39 天，性能表现优于手动设计的网络及现有 NAS 方法之最佳结果。