Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is
becoming an attractive solution in medical image segmentation. To make use of unlabeled …

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …

This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …

This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for
the reconstruction of images in positron emission tomography (PET). Deep learning can be …

Automatic segmentation is a fundamental task in computer-assisted medical image analysis.
Convolutional neural networks (CNNs) have been widely used for medical image …

Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …

Radiation therapy (RT) is regarded as the primary treatment for cancer in the clinic, aiming to
deliver an accurate dose to the planning target volume (PTV) while protecting the …