Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

Artificial intelligence (AI) now enables automated interpretation of medical images. However,
AI's potential use for interventional image analysis remains largely untapped. This is …

Generative adversarial networks (GANs) have become increasingly powerful, generating
mind-blowing photorealistic images that mimic the content of datasets they have been …

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 …

Due to the exponential growth of computational algorithms, artificial intelligence (AI)
methods are poised to improve the precision of diagnostic and therapeutic methods in …

We present an adversarial domain adaptation based deep learning approach for automatic
tumor segmentation from T2-weighted MRI. Our approach is composed of two steps:(i) a …

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each
contrast provides complementary information. However, the availability of each imaging …

Artificial intelligence in health care has experienced remarkable innovation and progress in
the last decade. Significant advancements can be attributed to the utilization of artificial …

Many clinical applications based on deep learning and pertaining to radiology have been
proposed and studied in radiology for classification, risk assessment, segmentation tasks …