UXNet：搜索多层次特征聚合的三维医学图像分割
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
摘要：
聚集多层次特征表示对于实现鲁棒的体积医学图像分割至关重要，对于辅助诊断和治疗非常重要。与最近的神经结构搜索（NAS）方法，通常在每个网络层中搜索最佳的运营商，但错过了一个很好的策略来搜索特征聚合，本文提出了一种新的NAS方法的3D医学图像分割，命名为UXNet，它搜索两个尺度的特征聚合策略，以及在编码器-解码器网络的分块操作。UXNet有几个吸引人的好处。(1)它显着提高了灵活性的经典UNet架构，它只聚集在等效分辨率的编码器和解码器的特征表示。(2)精心设计了UXNet的连续松弛，使其搜索方案以有效的可微方式执行。(3)大量的实验证明了UXNet与最近的NAS方法相比用于医学图像分割的有效性。UXNet发现的架构在几个公共3D医学图像分割基准的Dice方面优于现有的最先进模型，特别是对于边界位置和微小组织。UXNet的搜索计算复杂性很低，能够在两个TitanXP GPU上搜索不到1.5天的最佳性能网络。
英文摘要：
Aggregating multi-level feature representation plays a critical role in achieving robust volumetric medical image segmentation, which is important for the auxiliary diagnosis and treatment. Unlike the recent neural architecture search (NAS) methods that typically searched the optimal operators in each network layer, but missed a good strategy to search for feature aggregations, this paper proposes a novel NAS method for 3D medical image segmentation, named UXNet, which searches both the scale-wise feature aggregation strategies as well as the block-wise operators in the encoder-decoder network. UXNet has several appealing benefits. (1) It significantly improves flexibility of the classical UNet architecture, which only aggregates feature representations of encoder and decoder in equivalent resolution. (2) A continuous relaxation of UXNet is carefully designed, enabling its searching scheme performed in an efficient differentiable manner. (3) Extensive experiments demonstrate the effectiveness of UXNet compared with recent NAS methods for medical image segmentation. The architecture discovered by UXNet outperforms existing state-of-the-art models in terms of Dice on several public 3D medical image segmentation benchmarks, especially for the boundary locations and tiny tissues. The searching computational complexity of UXNet is cheap, enabling to search a network with the best performance less than 1.5 days on two TitanXP GPUs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
