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 …

Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Imputation of missing images via source-to-target modality translation can improve diversity
in medical imaging protocols. A pervasive approach for synthesizing target images involves …

Synthetic data in machine learning for medicine and healthcare | Nature Biomedical Engineering
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Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

Brain tumor is one of the most dangerous cancers in people of all ages, and its grade
recognition is a challenging problem for radiologists in health monitoring and automated …

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 …

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in
hospitals across the world. These have the potential to revolutionize our understanding of …

Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …