SOTAVerified

De-identification

De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.

Papers

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Showing 41–50 of 174 papers

TitleStatusHype
VerA: Versatile Anonymization Applicable to Clinical Facial Photographs—0
De-identification of clinical free text using natural language processing: A systematic review of current approaches—0
Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification—0
Privacy Protection in MRI Scans Using 3D Masked Autoencoders—0
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion ModelsCode1
Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging—0
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis—0
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistencyCode0
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market DomainCode1
In the Name of Fairness: Assessing the Bias in Clinical Record De-identificationCode0
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