Summary of Polypnextlstm: a Lightweight and Fast Polyp Video Segmentation Network Using Convnext and Convlstm, by Debayan Bhattacharya et al.
PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM
by Debayan Bhattacharya, Konrad Reuter, Finn Behrendt, Lennart Maack, Sarah Grube, Alexander Schlaefer
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This research proposes a novel deep learning model, PolypNextLSTM, which leverages video-based data to improve polyp segmentation accuracy. By harnessing temporal information, the model reduces parameter overhead and achieves superior performance compared to existing image-based and video-based models. The architecture employs a UNet-like structure with ConvNext-Tiny as its backbone, strategically omitting the last two layers to reduce parameters. A Convolutional Long Short Term Memory (ConvLSTM) module is used for temporal fusion. The model surpasses state-of-the-art models in both image and video-based approaches, achieving a Dice score of 0.7898 on the hard-to-detect polyp test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research proposes a new way to find polyps in medical images using videos. This helps doctors diagnose polyps more accurately. The method uses a special kind of artificial intelligence that looks at videos and finds patterns that help it identify polyps better than previous methods. The researchers tested their method on real video data and found that it works much better than existing approaches. |
Keywords
* Artificial intelligence * Deep learning * Unet
