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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 1–25 of 174 papers

TitleStatusHype
Accurate clinical and biomedical Named entity recognition at scaleCode3
CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image FormatsCode2
Enhancing the De-identification of Personally Identifiable Information in Educational DataCode1
De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient PrivacyCode1
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data SharingCode1
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion ModelsCode1
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market DomainCode1
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4Code1
RiDDLE: Reversible and Diversified De-identification with Latent EncryptorCode1
Hiding Visual Information via Obfuscating Adversarial PerturbationsCode1
DeID-VC: Speaker De-identification via Zero-shot Pseudo Voice ConversionCode1
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical TextsCode1
Radiology Text Analysis System (RadText): Architecture and EvaluationCode1
The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text AnonymizationCode1
Ego4D: Around the World in 3,000 Hours of Egocentric VideoCode1
Speech Pseudonymisation Assessment Using Voice Similarity MatricesCode1
MASK: A flexible framework to facilitate de-identification of clinical textsCode1
CIAGAN: Conditional Identity Anonymization Generative Adversarial NetworksCode1
Face Identity Disentanglement via Latent Space MappingCode1
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical RecordsCode1
Synthesis of Realistic ECG using Generative Adversarial NetworksCode1
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification—0
Re-identification of De-identified Documents with Autoregressive Infilling—0
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes—0
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