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

TL;DR

This paper introduces lightweight, plug-and-play decoders based on neural memory ODEs for U-like networks, significantly reducing parameters and FLOPs while maintaining segmentation performance in medical imaging.

Contribution

It proposes novel discretized nmODE decoders that are adaptable to various U-like networks, improving efficiency without sacrificing accuracy.

Findings

Parameters reduced by 20% to 50%.

FLOPs decreased by up to 74%.

Maintains performance across multiple datasets.

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 of these problems, but they often have pre-designed structures and consist of inseparable 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 Equations (nmODEs). These decoders integrate features at various levels of abstraction by processing information from skip connections and performing numerical operations on upward path. 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 FLOPs while maintaining performance. In summary, the proposed discretized nmODEs decoders are capable of reducing the number of parameters by about 20% ~ 50% and FLOPs by up to 74%, while possessing the potential to adapt to all U-like networks. Our code is available at https://github.com/nayutayuki/Lightweight-nmODE-Decoders-For-U-like-networks.