Medical Manifestation-Aware De-Identification
Yuan Tian, Shuo Wang, Guangtao Zhai
Abstract
Face de-identification (DeID) has been widely studied for common scenes, but remains under-researched for medical scenes, mostly due to the lack of large-scale patient face datasets. In this paper, we release MeMa, consisting of over 40,000 photo-realistic patient faces. MeMa is re-generated from massive real patient photos. By carefully modulating the generation and data-filtering procedures, MeMa avoids breaching real patient privacy, while ensuring rich and plausible medical manifestations. We recruit expert clinicians to annotate MeMa with both coarse- and fine-grained labels, building the first medical-scene DeID benchmark. Additionally, we propose a baseline approach for this new medical-aware DeID task, by integrating data-driven medical semantic priors into the DeID procedure. Despite its conciseness and simplicity, our approach substantially outperforms previous ones.
BibTeX
@article{Tian_Wang_Zhai_2025, title={Medical Manifestation-Aware De-Identification}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34835}, DOI={10.1609/aaai.v39i25.34835}, abstractNote={Face de-identification (DeID) has been widely studied for common scenes, but remains under-researched for medical scenes, mostly due to the lack of large-scale patient face datasets. In this paper, we release MeMa, consisting of over 40,000 photo-realistic patient faces. MeMa is re-generated from massive real patient photos. By carefully modulating the generation and data-filtering procedures, MeMa avoids breaching real patient privacy, while ensuring rich and plausible medical manifestations. We recruit expert clinicians to annotate MeMa with both coarse- and fine-grained labels, building the first medical-scene DeID benchmark. Additionally, we propose a baseline approach for this new medical-aware DeID task, by integrating data-driven medical semantic priors into the DeID procedure. Despite its conciseness and simplicity, our approach substantially outperforms previous ones.}, number={25}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Tian, Yuan and Wang, Shuo and Zhai, Guangtao}, year={2025}, month={Apr.}, pages={26363-26372} }