Summary of An Attentive Dual-encoder Framework Leveraging Multimodal Visual and Semantic Information For Automatic Osahs Diagnosis, by Yingchen Wei et al.
An Attentive Dual-Encoder Framework Leveraging Multimodal Visual and Semantic Information for Automatic OSAHS Diagnosis
by Yingchen Wei, Xihe Qiu, Xiaoyu Tan, Jingjing Huang, Wei Chu, Yinghui Xu, Yuan Qi
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed multimodal dual encoder model integrates visual and language inputs for automated OSAHS diagnosis, overcoming limitations of existing deep learning methods using facial image analysis. The model balances data with randomOverSampler, extracts key facial features using attention grids, and converts physiological data into meaningful text. Cross-attention combines image and text data for better feature extraction, and ordered regression loss ensures stable learning. This approach achieves state-of-the-art performance in a four-class severity classification task, demonstrating 91.3% top-1 accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to diagnose obstructive sleep apnea-hypopnea syndrome (OSAHS) using artificial intelligence. Right now, diagnosing OSAHS is expensive and uncomfortable because it requires spending the night in a special sleep lab. The old ways of using computers to analyze facial images weren’t very good either. This team’s new method uses both pictures and words to help diagnose OSAHS more accurately and quickly. |
Keywords
» Artificial intelligence » Attention » Classification » Cross attention » Deep learning » Encoder » Feature extraction » Regression
