Deep learning has been a powerful tool for medical image analysis, but large amount of
high-quality labeled datasets are generally required to train deep learning models with …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

Aviation Internet of Things (AIoT) data sharing can create tremendous value for participants.
With the development of AIoT and intelligent civil aviation, data security and privacy …

Federated learning is an emerging technology that enables the decentralised training of
machine learning-based methods for medical image analysis across multiple sites while …

Recently, federated learning has become a powerful technique for medical image
classification due to its ability to utilize datasets from multiple clinical clients while satisfying …

Federated learning (FL) is a machine learning paradigm in which distributed local nodes
collaboratively train a central model without sharing individually held private data. Existing …

Heterogeneous data captured by different scanning devices and imaging protocols can
affect the generalization performance of the deep learning magnetic resonance (MR) …

Centralized training methods have shown promising results in MR image reconstruction, but
privacy concerns arise when gathering data from multiple institutions. Federated learning, a …

Deep learning is becoming increasingly popular and is being extensively used in the field of
medical image analysis. However, the privacy sensitivity of medical data limits the …