IEEE transactions on neural networks and learning systems
Jan 5, 2022
Face is one of the most attractive sensitive information in visual shared data. It is an urgent task to design an effective face deidentification method to achieve a balance between facial privacy protection and data utilities when sharing data. Most...
BACKGROUND: Advances in healthcare artificial intelligence (AI) are occurring rapidly and there is a growing discussion about managing its development. Many AI technologies end up owned and controlled by private entities. The nature of the implementa...
PURPOSE: Medical note de-identification is critical for the protection of private information and the security of data sharing in collaborative research. The task demands the complete removal of all patient names and other sensitive information such ...
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Nov 3, 2020
The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboratio...
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Nov 3, 2020
The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboratio...
Neural networks : the official journal of the International Neural Network Society
Sep 20, 2020
In this paper, we propose a new face de-identification method based on generative adversarial network (GAN) to protect visual facial privacy, which is an end-to-end method (herein, FPGAN). First, we propose FPGAN and mathematically prove its converge...
IEEE journal of biomedical and health informatics
Mar 12, 2020
The medical and machine learning communities are relying on the promise of artificial intelligence (AI) to transform medicine through enabling more accurate decisions and personalized treatment. However, progress is slow. Legal and ethical issues aro...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
De-identification of clinical text, the prerequisite of electronic clinical data reuse, is a typical named entity recogni tion (NER) problem. A number of state-of-the-art deep learning methods for NER, such as Bi-LSTM-CRF (bidirec tional long-short-t...
BMC medical informatics and decision making
Jan 30, 2020
BACKGROUND: Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been ...
BMC medical informatics and decision making
Dec 5, 2019
BACKGROUND: De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in de...
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