Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence
of disruptive technical advances and impressive experimental results, notably in the field of …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

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 …

Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

Acquiring images of the same anatomy with multiple different contrasts increases the
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …

Fusing multi-modality medical images, such as magnetic resonance (MR) imaging and
positron emission tomography (PET), can provide various anatomical and functional …

Magnetic resonance (MR) imaging is a widely used medical imaging protocol that can be
configured to provide different contrasts between the tissues in human body. By setting …

A key limitation of deep convolutional neural network (DCNN)-based image segmentation
methods is the lack of generalizability. Manually traced training images are typically required …

We propose a multi-input multi-output fully convolutional neural network model for MRI
synthesis. The model is robust to missing data, as it benefits from, but does not require …

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks
reproducibility between protocols and scanners. It has been shown that even when care is …