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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 51–60 of 174 papers

TitleStatusHype
SVIA: A Street View Image Anonymization Framework for Self-Driving ApplicationsCode0
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning TasksCode0
PHICON: Improving Generalization of Clinical Text De-identification Models via Data AugmentationCode0
De-identifying Free Text of Japanese Dummy Electronic Health Records—0
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?—0
De-identification without losing faces—0
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective—0
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text—0
Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records—0
De-identification of medical records using conditional random fields and long short-term memory networks—0
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