Medical Manifestation-Aware De-Identification

TL;DR

This paper introduces MeMa, a large-scale, privacy-preserving dataset of synthetic patient faces with medical features, and proposes a baseline method for face de-identification in medical scenes, advancing privacy protection in healthcare imaging.

Contribution

The creation of MeMa, the first large-scale medical face dataset with annotations, and a baseline de-identification method incorporating medical semantic priors.

Findings

MeMa contains over 40,000 synthetic patient faces.

The proposed method outperforms previous de-identification approaches.

The dataset and method enhance privacy in medical imaging applications.

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. Dataset is available at https://github.com/tianyuan168326/MeMa-Pytorch.