Summary of Medical Manifestation-aware De-identification, by Yuan Tian et al.
Medical Manifestation-Aware De-Identification
by Yuan Tian, Shuo Wang, Guangtao Zhai
First submitted to arxiv on: 14 Dec 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 The paper presents a new dataset, MeMa, containing over 40,000 photo-realistic patient faces generated from real patient photos. The dataset is designed to ensure privacy and includes annotations from expert clinicians with coarse- and fine-grained labels. This allows for the creation of the first medical-scene DeID benchmark. Additionally, the authors propose a baseline approach that incorporates data-driven medical semantic priors into the DeID procedure. The approach outperforms previous ones despite its simplicity. MeMa is available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset called MeMa that has lots of pictures of patient faces. These pictures are made to look like real photos, but they’re actually generated from real photos. This means the people in the pictures can’t be identified without knowing who they are first. Doctors and other medical experts helped label the pictures so we can use them to train computers to remove faces from medical images. The authors also came up with a way to do this that’s better than what others have tried before. |
