Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …

Abstract Background and Objective Multi-modal medical images, such as magnetic
resonance imaging (MRI) and positron emission tomography (PET), have been widely used …

With the advance of medical imaging technologies, multimodal images such as magnetic
resonance images (MRI) and positron emission tomography (PET) can capture subtle …

Combining multi-modality data for brain disease diagnosis such as Alzheimer's disease
(AD) commonly leads to improved performance than those using a single modality …

Because of the tumor with infiltrative growth, the glioma boundary is usually fused with the
brain tissue, which leads to the failure of accurately segmenting the brain tumor structure …

Accurate medical classification requires a large number of multi-modal data, and in many
cases, different feature types. Previous studies have shown promising results when using …

Generative adversarial networks (GANs) are one powerful type of deep learning models that
have been successfully utilized in numerous fields. They belong to the broader family of …

Objective The proper handling of missing values is critical to delivering reliable estimates
and decisions, especially in high-stakes fields such as clinical research. In response to the …

Deep learning techniques have been increasingly applied to the diagnosis of Alzheimer's
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …