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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 126–150 of 174 papers

TitleStatusHype
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
Language Resources to Support Language Diversity – the ELRA Achievements—0
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes—0
Large Language Models for Patient Comments Multi-Label Classification—0
Live Face De-Identification in Video—0
LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languages—0
MAPA Project: Ready-to-Go Open-Source Datasets and Deep Learning Technology to Remove Identifying Information from Text Documents—0
Medical Image Deidentification, Cleaning and Compression Using Pylogik—0
Named Entity Recognition in Unstructured Medical Text Documents—0
Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish—0
NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification—0
Open video data sharing in developmental and behavioural science—0
PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences—0
Palmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis—0
Performance of Automatic De-identification Across Different Note Types—0
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
De-identification of Privacy-related Entities in Job PostingsCode0
The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray GenerationCode0
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. PerformanceCode0
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language ModelsCode0
DEDUCE: A pattern matching method for automatic de-identification of Dutch medical textCode0
De-identification of Patient Notes with Recurrent Neural NetworksCode0
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistencyCode0
Publicly Available Clinical BERT EmbeddingsCode0
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