论文标题
MS-NAS：多尺度神经体系结构搜索医学图像分割
MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation
论文作者
论文摘要
神经体系结构搜索（NAS）的最新突破激发了医疗图像细分中的各种应用。但是，大多数现有的工作要么只是依靠高参数调整，要么坚持使用固定的网络主干，从而限制了基础搜索空间以识别更有效的体系结构。本文提出了一个多尺度NAS（MS-NAS）框架，该框架具有从网络骨干到单元操作的多尺度搜索空间，以及多尺度的融合功能以及具有不同尺寸的功能的融合功能。为了减轻由于较大的搜索空间而导致的计算开销，使用部分通道连接方案和两步解码方法来减少计算开销，同时保持优化质量。实验结果表明，在各种数据集中进行分割，MS-NAS的表现优于最先进的方法，并实现0.6-5.4％MIOU和0.4-3.5％DSC改进，而计算资源消耗却降低了18.0-24.9％。
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%.
