Summary of Xnet V2: Fewer Limitations, Better Results and Greater Universality, by Yanfeng Zhou et al.
XNet v2: Fewer Limitations, Better Results and Greater Universality
by Yanfeng Zhou, Lingrui Li, Zichen Wang, Guole Liu, Ziwen Liu, Ge Yang
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a novel architecture, XNet v2, which addresses the limitations of its predecessor, XNet. The main issue with XNet is that it degrades in performance when images lack high-frequency information and underutilizes raw images while insufficiently fusing them. To overcome these challenges, XNet v2 employs a wavelet-based image-level complementary fusion approach, incorporating fusion results along with raw images as inputs for three sub-networks to construct consistency loss. Additionally, the paper proposes a feature-level fusion module to enhance the transfer of low-frequency and high-frequency information. The results show that XNet v2 achieves state-of-the-art performance in semi-supervised segmentation while maintaining competitive results in fully-supervised learning, outperforming XNet in scenarios where it fails. Code is available at this https URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary XNet v2 is a new way to improve image segmentation in medical imaging. The old version, XNet, wasn’t very good when images didn’t have many details or when it used the wrong information from the images. To fix this, the new version combines different parts of the images and uses them as training data for three separate networks. This helps the model learn better and be more accurate. The results show that XNet v2 is much better than XNet in most cases. |
Keywords
* Artificial intelligence * Image segmentation * Semi supervised * Supervised
