AIMC Topic: Privacy
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Applying Deep Neural Networks over Homomorphic Encrypted Medical Data.
Computational and mathematical methods in medicine
Apr 9, 2020
In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regu...
Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.
Scientific reports
Mar 11, 2020
A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data lea...
Ethical dilemmas posed by mobile health and machine learning in psychiatry research.
Bulletin of the World Health Organization
Feb 25, 2020
The application of digital technology to psychiatry research is rapidly leading to new discoveries and capabilities in the field of mobile health. However, the increase in opportunities to passively collect vast amounts of detailed information on stu...
Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition.
Neural networks : the official journal of the International Neural Network Society
Feb 11, 2020
In recent years, deep learning achieves remarkable results in the field of artificial intelligence. However, the training process of deep neural networks may cause the leakage of individual privacy. Given the model and some background information of ...
Shaping technologies for older adults with and without dementia: Reflections on ethics and preferences.
Health informatics journal
Jan 23, 2020
As a result of several years of European funding, progressive introduction of assistive technologies in our society has provided many researchers and companies with opportunities to develop new information and communication technologies aimed at over...
The Future of Digital Communication: Improved Messaging Context, Artificial Intelligence, and Your Privacy.
Journal of the American College of Radiology : JACR
Jan 10, 2020
Distributed learning on 20 000+ lung cancer patients - The Personal Health Train.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jan 3, 2020
BACKGROUND AND PURPOSE: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) pro...
Privacy-enhanced multi-party deep learning.
Neural networks : the official journal of the International Neural Network Society
Oct 11, 2019
In multi-party deep learning, multiple participants jointly train a deep learning model through a central server to achieve common objectives without sharing their private data. Recently, a significant amount of progress has been made toward the priv...
KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services.
PloS one
Oct 3, 2019
BACKGROUND AND OBJECTIVE: To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in...
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