Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …

Deep MRI reconstruction is commonly performed with conditional models that de-alias
undersampled acquisitions to recover images consistent with fully-sampled data. Since …

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 …

Deep-learning models have enabled performance leaps in analysis of high-dimensional
functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for …

Multi-modal medical images provide complementary soft-tissue characteristics that aid in the
screening and diagnosis of diseases. However, limited scanning time, image corruption and …

Multimodal medical images have been widely applied in various clinical diagnoses and
treatments. Due to the practical restrictions, certain modalities may be hard to acquire …

In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets
leads to the degraded performance of models in the target sites. The traditional domain …

In recent years, deep learning models have shown their advantages in neuroimage
analysis, such as brain disease diagnosis. Unfortunately, it is usually difficult to acquire …

Objective: Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a
sensitive imaging technique critical for breast cancer diagnosis. However, the administration …

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the
reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting …