3D convolution neural networks (CNN) have been proved very successful in
parsing organs or tumours in 3D medical images, but it remains sophisticated
and time-consuming to choose or design proper 3D networks given different task
contexts. Recently, Neural Architecture Search (NAS) is proposed to solve this
problem by searching for the best network architecture automatically. However,
the inconsistency between search stage and deployment stage often exists in NAS
algorithms due to memory constraints and large search space, which could become
more serious when applying NAS to some memory and time consuming tasks, such as
3D medical image segmentation. In this paper, we propose coarse-to-fine neural
architecture search (C2FNAS) to automatically search a 3D segmentation network
from scratch without inconsistency on network size or input size. Specifically,
we divide the search procedure into two stages: 1) the coarse stage, where we
search the macro-level topology of the network, i.e. how each convolution
module is connected to other modules; 2) the fine stage, where we search at
micro-level for operations in each cell based on previous searched macro-level
topology. The coarse-to-fine manner divides the search procedure into two
consecutive stages and meanwhile resolves the inconsistency. We evaluate our
method on 10 public datasets from Medical Segmentation Decalthon (MSD)
challenge, and achieve state-of-the-art performance with the network searched
using one dataset, which demonstrates the effectiveness and generalization of
our searched models.

本论文提出了一种从零开始自动搜索 3D 医学图像分割网络的方法 —— 粗 - 细神经网络结构搜索（C2FNAS），该方法通过将搜索过程分解为宏观拓扑和微观细胞操作两个阶段来规避搜索和部署阶段之间的不一致，最后将该方法应用于 10 个公共数据集上获得了最新的实验结果。