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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 151–174 of 174 papers

TitleStatusHype
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training—0
Audio De-identification: A New Entity Recognition Task—0
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)—0
De-identification without losing faces—0
Automatic end-to-end De-identification: Is high accuracy the only metric?—0
Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels—0
A survey of automatic de-identification of longitudinal clinical narratives—0
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation—0
De-identifying Free Text of Japanese Dummy Electronic Health Records—0
DEDUCE: A pattern matching method for automatic de-identification of Dutch medical textCode0
Using routinely collected patient data to support clinical trials research in accountable care organizations—0
Privacy-Protective-GAN for Face De-identification—0
Natural Language Generation for Electronic Health RecordsCode0
Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes—0
De-identification of medical records using conditional random fields and long short-term memory networks—0
Plausible Deniability for Privacy-Preserving Data Synthesis—0
Transfer Learning for Named-Entity Recognition with Neural Networks—0
De-identification In practice—0
Detection of Text Reuse in French Medical Corpora—0
Deep Learning Architecture for Patient Data De-identification in Clinical Records—0
Feature-Augmented Neural Networks for Patient Note De-identification—0
De-identification of Patient Notes with Recurrent Neural NetworksCode0
Towards Reversible De-Identification in Video Sequences Using 3D Avatars and Steganography—0
De-Identification of Clinical Free Text in Dutch with Limited Training Data: A Case Study—0
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