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 …

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 …

This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …

Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …

This brief review summarizes the major applications of artificial intelligence (AI), in particular
deep learning approaches, in molecular imaging and radiation therapy research. To this …

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

Techniques from the field of artificial intelligence, and more specifically machine (deep)
learning methods, have been core components of most recent developments in the field of …

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET
attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their …

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of
diagnosis and staging, refined surveillance strategies, and introduced specific and …

The rapid expansion of machine learning is offering a new wave of opportunities for nuclear
medicine. This paper reviews applications of machine learning for the study of attenuation …