Summary of Akgnet: Attribute Knowledge-guided Unsupervised Lung-infected Area Segmentation, by Qing En et al.
AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area Segmentation
by Qing En, Yuhong Guo
First submitted to arxiv on: 17 Apr 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 proposes a novel framework, AKGNet, for unsupervised lung-infected area segmentation from multi-modal image-text data without mask annotations. The approach leverages attribute knowledge-guided learning, cross-attention fusion, and pseudo-label exploration to refine the segmentation mask. Key components include text attribute knowledge learning, attribute-image cross-attention fusion, and self-training mask improvement. Experimental results demonstrate superior performance compared to state-of-the-art methods on a benchmark medical image dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lung diseases are serious health issues that require accurate assessments of lung-infected areas. This research develops a new way to identify these areas using only images and text without needing experts to label the data first. The method, called AKGNet, learns from both the images and text to find the infected areas. It’s like having a smart AI doctor that can look at medical images and text reports to diagnose lung diseases more accurately. |
Keywords
» Artificial intelligence » Cross attention » Mask » Multi modal » Self training » Unsupervised
