The recurrent mechanism has recently been introduced into U-Net in various
medical image segmentation tasks. Existing studies have focused on promoting
network recursion via reusing building blocks. Although network parameters
could be greatly saved, computational costs still increase inevitably in
accordance with the pre-set iteration time. In this work, we study a
multi-scale upgrade of a bi-directional skip connected network and then
automatically discover an efficient architecture by a novel two-phase Neural
Architecture Search (NAS) algorithm, namely BiX-NAS. Our proposed method
reduces the network computational cost by sifting out ineffective multi-scale
features at different levels and iterations. We evaluate BiX-NAS on two
segmentation tasks using three different medical image datasets, and the
experimental results show that our BiX-NAS searched architecture achieves the
state-of-the-art performance with significantly lower computational cost.

本研究提出了一种名为 BiX-NAS 的新型双向跳过连接网络，通过多尺度升级和神经架构搜索算法自动发现高效的结构，从而减少计算成本并在三个医学图像数据集上获得了最新性能。