A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations for Slimming the Decoder

Abstract

In recent years, advanced U-like networks have demonstrated remarkable performance in medical image segmentation tasks. However, their drawbacks, including excessive parameters, high computational complexity, and slow inference speed, pose challenges for practical implementation in scenarios with limited computational resources. Existing lightweight U-like networks have alleviated some problems, but they often have pre-designed structures and consist of non-detachable modules, limiting their application scenarios. In this paper, we propose three plug-and-play decoders by employing different discretization methods of the neural memory Ordinary Differential Equation (nmODE). These decoders integrate features at various levels of abstraction by processing information from skip connections and performing numerical operations on upward paths. Through experiments on the PH2, ISIC2017, and ISIC2018 datasets, we embed these decoders into different U-like networks, demonstrating their effectiveness in significantly reducing the number of parameters and computation while maintaining performance. In summary, the proposed discretized nmODE decoder is capable of reducing the number of parameters by about 20% ~ 50% and computation by up to 74%, while being adaptive to all U-like networks. Our code is available at https://github.com/nayutayuki/Lightweight-nmODE-Decoders-For-U-like-networks.