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 …

U-shaped networks have become prevalent in various medical image tasks such as
segmentation, and restoration. However, most existing U-shaped networks rely on …

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 …

Magnetic resonance imaging (MRI) is extensively utilized in clinical practice for diagnostic
purposes, owing to its non-invasive nature and remarkable ability to provide detailed …

Palmprint as biometrics has gained increasing attention recently due to its discriminative
ability and robustness. However, existing methods mainly improve palmprint verification …

The automatic segmentation of organs or tissues is crucial for early diagnosis and treatment.
Existing deep learning methods either need massive annotation data or use Unsupervised …

Recently, artificial intelligence has been widely used in intelligent disease diagnosis and
has achieved great success. However, most of the works mainly rely on the extraction of …

U-shaped networks are widely used in various medical image tasks, such as segmentation,
restoration and reconstruction, but most of them usually rely on centralized learning and thus …

Abstract Effective communication among Fog Computing resources is crucial concerning the
network's diverse Quality of Service (QoS) parameters. However, while Fog nodes may be …

COVID-19-like pandemics are a major threat to the global health system that causes a lot of
deaths across ages. Large-scale medical images (ie, X-rays, computed tomography (CT)) …