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

Quansong He, Xiaojun Yao, Jun Wu, Zhang Yi, Tao He

TL;DR

This work tackles the practical deployment bottlenecks of U-like medical image segmentation networks by introducing three discretized nmODE decoders that replace traditional decoders. The nmODEs use an external input from skip connections and a memory-augmented update $\dot{y}(t) = -y(t) + f\big(y(t) + g\big(x(t), \theta_t\big)\big)$, discretized via explicit Euler, Heun, or linear multistep methods to yield lightweight decoders that share parameters along the upward path. Across PH2, ISIC2017, and ISIC2018, the nmODE decoders reduce parameters by roughly $20$–$50\%$ and FLOPs by up to $74\%$, while often preserving or improving segmentation metrics such as mIoU and DSC, and achieving state-of-the-art performance in some configurations. The approach is demonstrated as universally applicable to a range of U-like networks and is accompanied by a public code release for broader adoption.

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.

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

TL;DR

This work tackles the practical deployment bottlenecks of U-like medical image segmentation networks by introducing three discretized nmODE decoders that replace traditional decoders. The nmODEs use an external input from skip connections and a memory-augmented update , discretized via explicit Euler, Heun, or linear multistep methods to yield lightweight decoders that share parameters along the upward path. Across PH2, ISIC2017, and ISIC2018, the nmODE decoders reduce parameters by roughly – and FLOPs by up to , while often preserving or improving segmentation metrics such as mIoU and DSC, and achieving state-of-the-art performance in some configurations. The approach is demonstrated as universally applicable to a range of U-like networks and is accompanied by a public code release for broader adoption.

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.

Paper Structure

This paper contains 15 sections, 3 theorems, 10 equations, 10 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. UNet and Its Variants
  4. Neural Ordinary Differential Equations
  5. Methods
  6. The U-like Networks with nmODEs Decoders
  7. Discretization Methods
  8. Function Selection & Initial Value Problem
  9. Experiments
  10. Datasets
  11. Implementation Details
  12. Comparative Results
  13. Representative Results
  14. Ablation Study
  15. Conclusion

Key Result

Theorem 1

Explicit Euler's Method euler1845institutionum. Given the derivative $\Dot{y}(t)=F(t, y(t))$, choose a value $\delta$ for the size of every step along t-axis and set $t_{n+1}=t_n+\delta$. The $y_{n+1}$ from $y_n$ and $t_n$ is where $y_n$ is an approximate solution at time $t_n$, i.e., $y_n \approx y(t_n)$. $y_{n+1}$ is an explicit function of $y_i$ for $i\leq n$.

Figures (10)

  • Figure 1: mIoU on PH2.
  • Figure 2: mIoU on ISIC2018.
  • Figure 4: The U-like networks (with the proposed discretized nmODEs decoders). The upward path of the nmODEs decoders share the same parameters among layers without upsampling operations.
  • Figure 5: NODE block.
  • Figure 6: nmODE block.
  • ...and 5 more figures

Theorems & Definitions (3)

  • Theorem 1
  • Theorem 2
  • Theorem 3