  Considering the scarcity of medical data, most datasets in medical image
analysis are an order of magnitude smaller than those of natural images.
However, most Network Architecture Search (NAS) approaches in medical images
focused on specific datasets and did not take into account the generalization
ability of the learned architectures on unseen datasets as well as different
domains. In this paper, we address this point by proposing to search for
generalizable U-shape architectures on a composited dataset that mixes medical
images from multiple segmentation tasks and domains creatively, which is named
MixSearch. Specifically, we propose a novel approach to mix multiple
small-scale datasets from multiple domains and segmentation tasks to produce a
large-scale dataset. Then, a novel weaved encoder-decoder structure is designed
to search for a generalized segmentation network in both cell-level and
network-level. The network produced by the proposed MixSearch framework
achieves state-of-the-art results compared with advanced encoder-decoder
networks across various datasets.
