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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 26–50 of 174 papers

TitleStatusHype
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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