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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 1–50 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
CIAGAN: Conditional Identity Anonymization Generative Adversarial NetworksCode1
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical TextsCode1
Face Identity Disentanglement via Latent Space MappingCode1
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical RecordsCode1
RiDDLE: Reversible and Diversified De-identification with Latent EncryptorCode1
Enhancing the De-identification of Personally Identifiable Information in Educational DataCode1
The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text AnonymizationCode1
MASK: A flexible framework to facilitate de-identification of clinical textsCode1
Speech Pseudonymisation Assessment Using Voice Similarity MatricesCode1
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4Code1
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market DomainCode1
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data SharingCode1
DeID-VC: Speaker De-identification via Zero-shot Pseudo Voice ConversionCode1
Ego4D: Around the World in 3,000 Hours of Egocentric VideoCode1
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion ModelsCode1
Radiology Text Analysis System (RadText): Architecture and EvaluationCode1
De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient PrivacyCode1
Hiding Visual Information via Obfuscating Adversarial PerturbationsCode1
Synthesis of Realistic ECG using Generative Adversarial NetworksCode1
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation—0
AnonymousNet: Natural Face De-Identification with Measurable Privacy—0
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective—0
Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts—0
Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets—0
Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records—0
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text—0
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?—0
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records—0
Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification—0
De-identification In practice—0
Automatic end-to-end De-identification: Is high accuracy the only metric?—0
An Overview of AI and Blockchain Integration for Privacy-Preserving—0
De-identification without losing faces—0
A Context-Enhanced De-identification System—0
Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning—0
De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks—0
Audio De-identification - a New Entity Recognition Task—0
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis—0
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms—0
Data-Constrained Synthesis of Training Data for De-Identification—0
DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation—0
Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation—0
Deep Learning Architecture for Patient Data De-identification in Clinical Records—0
An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents—0
Cross-Clinic De-Identification of Swedish Electronic Health Records: Nuances and Caveats—0
De-identification is not always enough—0
De-Identification of Clinical Free Text in Dutch with Limited Training Data: A Case Study—0
Audio De-identification: A New Entity Recognition Task—0
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