[HTML][HTML] Privacy-preserving artificial intelligence in healthcare: Techniques and applications
There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …
clinically-validated applications to improve the performance, capacity, and efficacy of …
A review on client-server attacks and defenses in federated learning
A Sharma, N Marchang - Computers & Security, 2024 - Elsevier
Federated Learning (FL) offers decentralized machine learning (ML) capabilities while
potentially safeguarding data privacy. However, this architecture introduces unique security …
potentially safeguarding data privacy. However, this architecture introduces unique security …
Reconciling privacy and accuracy in AI for medical imaging
A Ziller, TT Mueller, S Stieger, LF Feiner… - Nature Machine …, 2024 - nature.com
Artificial intelligence (AI) models are vulnerable to information leakage of their training data,
which can be highly sensitive, for example, in medical imaging. Privacy-enhancing …
which can be highly sensitive, for example, in medical imaging. Privacy-enhancing …
Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging
S Tayebi Arasteh, A Ziller, C Kuhl, M Makowski… - Communications …, 2024 - nature.com
Background Artificial intelligence (AI) models are increasingly used in the medical domain.
However, as medical data is highly sensitive, special precautions to ensure its protection are …
However, as medical data is highly sensitive, special precautions to ensure its protection are …
Dpd-fvae: Synthetic data generation using federated variational autoencoders with differentially-private decoder
B Pfitzner, B Arnrich - arXiv preprint arXiv:2211.11591, 2022 - arxiv.org
Federated learning (FL) is getting increased attention for processing sensitive, distributed
datasets common to domains such as healthcare. Instead of directly training classification …
datasets common to domains such as healthcare. Instead of directly training classification …
[HTML][HTML] Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data
Summary Background Machine Learning (ML) has demonstrated its great potential on
medical data analysis. Large datasets collected from diverse sources and settings are …
medical data analysis. Large datasets collected from diverse sources and settings are …
Medical Imaging Applications of Federated Learning
Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL)
to several domains ranging from edge computing to banking. The technique's inherent …
to several domains ranging from edge computing to banking. The technique's inherent …
Differentially private and explainable boosting machine with enhanced utility
I Baek, YD Chung - Neurocomputing, 2024 - Elsevier
In this paper, we introduce DP-EBM*, an enhanced utility version of the Differentially Private
Explainable Boosting Machine (DP-EBM). DP-EBM* offers predictions for both classification …
Explainable Boosting Machine (DP-EBM). DP-EBM* offers predictions for both classification …
Federated Learning in Medical Image Analysis: A Systematic Survey
FR da Silva, R Camacho, JMRS Tavares - Electronics, 2023 - mdpi.com
Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically,
hospitals maintain vast repositories of images, which can be leveraged for various purposes …
hospitals maintain vast repositories of images, which can be leveraged for various purposes …
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
A Ziller, TT Mueller, S Stieger, L Feiner, J Brandt… - arXiv preprint arXiv …, 2023 - arxiv.org
Artificial Intelligence (AI) models are vulnerable to information leakage of their training data,
which can be highly sensitive, for example in medical imaging. Privacy Enhancing …
which can be highly sensitive, for example in medical imaging. Privacy Enhancing …