Summary of A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations For Slimming the Decoder, by Quansong He et al.
A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations for Slimming the Decoder
by Quansong He, Xiaojun Yao, Jun Wu, Zhang Yi, Tao He
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to address the limitations of advanced U-like networks in medical image segmentation tasks. These models have shown remarkable performance but are hindered by excessive parameters, high computational complexity, and slow inference speed, making them impractical for scenarios with limited resources. To overcome these challenges, the authors propose three plug-and-play decoders that employ different discretization methods based on neural memory Ordinary Differential Equations (nmODEs). These decoders process information from skip connections and perform numerical operations on upward paths to integrate features at various levels of abstraction. The proposed decoders are evaluated on the PH2, ISIC2017, and ISIC2018 datasets, demonstrating their effectiveness in reducing the number of parameters and floating-point operations (FLOPs) while maintaining performance. The authors claim that these decoders can reduce parameter counts by 20-50% and FLOPs by up to 74%, making them adaptable to various U-like networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers aim to make advanced image segmentation models more practical for use in medical settings. Right now, these models are very good at their job but require a lot of computer power to work quickly. The scientists propose some new “decoders” that can be easily added to existing models and still work well. They tested these decoders on several different datasets and found that they were able to reduce the amount of information needed for the model to work, while still getting good results. This could make it possible to use these models in situations where computer power is limited. |
Keywords
» Artificial intelligence » Image segmentation » Inference
