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The recurrent mechanism has recently been introduced into U-Net in variousmedical image segmentation tasks. Existing studies have focused on promotingnetwork recursion via reusing building blocks. Although network parameterscould be greatly saved, computational costs still increase inevitably inaccordance with the pre-set iteration time. In this work, we study amulti-scale upgrade of a bi-directional skip connected network and thenautomatically discover an efficient architecture by a novel two-phase NeuralArchitecture Search (NAS) algorithm, namely BiX-NAS. Our proposed methodreduces the network computational cost by sifting out ineffective multi-scalefeatures at different levels and iterations. We evaluate BiX-NAS on twosegmentation tasks using three different medical image datasets, and theexperimental results show that our BiX-NAS searched architecture achieves thestate-of-the-art performance with significantly lower computational cost.

BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation