The recent breakthroughs of Neural Architecture Search (NAS) have motivated
various applications in medical image segmentation. However, most existing work
either simply rely on hyper-parameter tuning or stick to a fixed network
backbone, thereby limiting the underlying search space to identify more
efficient architecture. This paper presents a Multi-Scale NAS (MS-NAS)
framework that is featured with multi-scale search space from network backbone
to cell operation, and multi-scale fusion capability to fuse features with
different sizes. To mitigate the computational overhead due to the larger
search space, a partial channel connection scheme and a two-step decoding
method are utilized to reduce computational overhead while maintaining
optimization quality. Experimental results show that on various datasets for
segmentation, MS-NAS outperforms the state-of-the-art methods and achieves
0.6-5.4% mIOU and 0.4-3.5% DSC improvements, while the computational resource
consumption is reduced by 18.0-24.9%.

本文提出了一种 Multi-Scale NAS 框架，具有从网络主干到单元操作的多尺度搜索空间和多尺度融合能力，采用部分通道连接方案和两步解码方法来提高计算效率，实验结果表明，MS-NAS 在各种分割数据集上优于现有方法，并实现了 0.6-5.4% mIOU 和 0.4-3.5% DSC 的提高，计算资源消耗减少了 18.0-24.9%。