Cross-modality image synthesis is an active research topic with multiple medical clinically
relevant applications. Recently, methods allowing training with paired but misaligned data …

Many clinical works require medical inter-modality imaging results since the supplementary
imaging information from different modalities can be combined to provide better decision …

In real-world clinical settings, traditional deep learning-based classification methods
struggle with diagnosing newly introduced disease types because they require samples …

Recent advances in MRI‐guided radiation therapy (MRgRT) and deep learning techniques
encourage fully adaptive radiation therapy (ART), real‐time MRI monitoring, and the MRI …

This paper presents a comprehensive systematic review of generative models (GANs, VAEs,
DMs, and LLMs) used to synthesize various medical data types, including imaging …

We have witnessed substantial growth in super-resolution research within the computer
vision community. Unlike previous works that mainly focus on the super-resolution synthesis …

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based
synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for …

Medical images are usually degraded by numerous noises during acquisition or
transmission, which often causes low contrast leading to deterioration of image quality. As …

The risk of malicious deep synthesis lurks in hedonic applications, yet people tend to be
ethically tolerant, leaving governance in a quandary. This study explores the reason for this …

Positron emission tomography (PET) has been widely used in nuclear medicine to diagnose
cancer. PET images suffer from degradation because of the scanner's physical limitations …