Despite technological and medical advances, the detection, interpretation, and treatment of
cancer based on imaging data continue to pose significant challenges. These include inter …

In the ongoing COVID-19 pandemic, digital technologies have played a vital role to minimize
the spread of COVID-19, and to control its pitfalls for the general public. Without such …

The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in
the evolution of information technology. With AIGC, it can be effortless to generate high …

In recent years, considerable work has been conducted on the development of synthetic
medical images, but there are no satisfactory methods for evaluating their medical suitability …

Objective 3D modeling is a major challenge in computer-assisted surgery (CAS). Manual
segmentation, as the gold standard, is tedious, time consuming, and particularly challenging …

Large annotated datasets are required for training deep learning models, but in medical
imaging data sharing is often complicated due to ethics, anonymization and data protection …

Inevitable domain and task discrepancies in real-world scenarios can impair the
generalization performance of the pre-trained deep models for medical data. Therefore, we …

Applications that could benefit from automatic understanding of human-human
conversations often come with challenges associated with private information in real-world …

Generative adversarial networks (GAN) represent two deep learning (DL) models positioned
in an adversarial manner to generate and evaluate images. This area of research promises …

Crystalline defects, such as line-like dislocations, play an important role for the performance
and reliability of many metallic devices. Their interaction and evolution still poses a multitude …