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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 101–150 of 174 papers

TitleStatusHype
ToonerGAN: Reinforcing GANs for Obfuscating Automated Facial Indexing—0
Toward Face Biometric De-identification using Adversarial Examples—0
Towards a Data Privacy-Predictive Performance Trade-off—0
Towards De-identification of Legal Texts—0
Towards Privacy-Preserving Person Re-identification via Person Identify Shift—0
Towards Reversible De-Identification in Video Sequences Using 3D Avatars and Steganography—0
Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes—0
Transferability of Neural Network Clinical De-identification Systems—0
Transfer Learning for Named-Entity Recognition with Neural Networks—0
Using routinely collected patient data to support clinical trials research in accountable care organizations—0
Utility Preservation of Clinical Text After De-Identification—0
VerA: Versatile Anonymization Applicable to Clinical Facial Photographs—0
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)—0
Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images—0
CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulation—0
Face De-identification: State-of-the-art Methods and Comparative Studies—0
Feature-Augmented Neural Networks for Patient Note De-identification—0
FICGAN: Facial Identity Controllable GAN for De-identification—0
Fidelity and Privacy of Synthetic Medical Data—0
Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging—0
GiusBERTo: A Legal Language Model for Personal Data De-identification in Italian Court of Auditors Decisions—0
HB Deid - HB De-identification tool demonstrator—0
Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification—0
IdentityDP: Differential Private Identification Protection for Face Images—0
Medical records condensation: a roadmap towards healthcare data democratisation—0
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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