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

TL;DR

BiX-NAS introduces a two-phase neural architecture search method to optimize bi-directional networks for medical image segmentation, significantly reducing computational costs while achieving state-of-the-art accuracy.

Contribution

This work presents a novel two-phase NAS algorithm for designing efficient bi-directional architectures tailored for medical image segmentation tasks.

Findings

Achieves state-of-the-art segmentation performance

Reduces computational cost compared to existing methods

Effective multi-scale feature selection across different levels

Abstract

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.