Summary of Otcxr: Rethinking Self-supervised Alignment Using Optimal Transport For Chest X-ray Analysis, by Vandan Gorade et al.
OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis
by Vandan Gorade, Azad Singh, Deepak Mishra
First submitted to arxiv on: 18 Apr 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 A novel self-supervised learning framework, OTCXR, is proposed for analyzing medical modalities like X-rays without annotations. Conventional SSL methods struggle with semantic alignment and capturing subtle details, limiting their ability to accurately represent anatomical structures and pathological features. OTCXR leverages optimal transport (OT) to learn dense semantic invariance, integrating OT with the Cross-Viewpoint Semantics Infusion Module (CV-SIM). This approach enhances the model’s ability to capture local spatial features and global contextual dependencies across different viewpoints. OTCXR also incorporates variance and covariance regularizations within the OT framework to prioritize clinically relevant information while suppressing less informative features. The efficacy of OTCXR is validated through comprehensive experiments on three publicly available chest X-ray datasets, demonstrating its superiority over state-of-the-art methods across all evaluated tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn from medical images like X-rays without needing labels is proposed. This method, called OTCXR, helps the model understand what’s important and what’s not by comparing different views of the same image. It’s better than other methods at recognizing patterns in the images and can be used for tasks like diagnosing diseases. The results show that OTCXR works well on three different datasets. |
Keywords
» Artificial intelligence » Alignment » Self supervised » Semantics
