Summary of Masked Latent Transformer with the Random Masking Ratio to Advance the Diagnosis Of Dental Fluorosis, by Yun Wu and Hao Xu and Maohua Gu and Zhongchuan Jiang and Jun Xu and Youliang Tian
Masked Latent Transformer with the Random Masking Ratio to Advance the Diagnosis of Dental Fluorosis
by Yun Wu, Hao Xu, Maohua Gu, Zhongchuan Jiang, Jun Xu, Youliang Tian
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 A deep learning-based approach to diagnosing dental fluorosis is proposed in this paper, which aims to improve the accuracy of early non-invasive diagnosis of endemic fluorosis. The authors construct an open-source dental fluorosis image dataset (DFID) and develop a pioneering deep learning model called masked latent transformer with random masking ratio (MLTrMR). MLTrMR employs a mask latent modeling scheme based on Vision Transformer to enhance contextual learning of dental fluorosis lesion characteristics. The model achieves state-of-the-art performance on the DFID, with an accuracy of 80.19%, F1 score of 75.79%, and quadratic weighted kappa of 81.28%. This approach has the potential to improve diagnosis of dental fluorosis and ultimately enhance patient care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dental fluorosis is a condition caused by too much fluoride in the diet, which can affect the appearance of tooth enamel. Doctors need to be able to diagnose it early on without having to do any tests or take X-rays. Right now, there isn’t much research being done on using artificial intelligence to help with this diagnosis. So, researchers created a special set of images called DFID (Dental Fluorosis Image Dataset) that can be used to train AI models to recognize dental fluorosis. They also developed a new type of AI model called MLTrMR (Masked Latent Transformer with Random Masking Ratio). This model is good at learning patterns in images and making predictions. The researchers tested their model on the DFID and found that it was really good at diagnosing dental fluorosis, even better than other models that have been developed. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Mask » Transformer » Vision transformer
