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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 31–40 of 174 papers

TitleStatusHype
Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts—0
Zero-shot generation of synthetic neurosurgical data with large language modelsCode0
The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray GenerationCode0
Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images—0
SVIA: A Street View Image Anonymization Framework for Self-Driving ApplicationsCode0
Medical Manifestation-Aware De-IdentificationCode0
LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languages—0
Face De-identification: State-of-the-art Methods and Comparative Studies—0
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal MessagesCode0
Large Language Models for Patient Comments Multi-Label Classification—0
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