Radiomics, the high-throughput extraction of quantitative imaging features from medical
images, holds immense potential for advancing precision medicine in oncology and beyond …

Purpose Image artefacts continue to pose challenges in clinical molecular imaging, resulting
in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and …

Objective: The primary objective of our study is to address the challenge of confidentially
sharing medical images across different centers. This is often a critical necessity in both …

Biomedical imaging plays a critical role in early detection, precise diagnosis, treatment
planning, and monitoring responses, but traditional methods encounter challenges such as …

Abstract The World Health Organization (WHO) proclaimed the coronavirus of 2019 (COVID-
19) a global pandemic in March 2020. Effective testing is essential to stop the epidemic from …

Abstract Background and Objective Lung tumor annotation is a key upstream task for further
diagnosis and prognosis. Although deep learning techniques have promoted automation of …

Background Notwithstanding the encouraging results of previous studies reporting on the
efficiency of deep learning (DL) in COVID‐19 prognostication, clinical adoption of the …

In this study, we investigated the application of distributed learning, including federated
learning and cyclical weight transfer, in the development of computer-aided detection …

Purpose Medical imaging artifacts compromise image quality and quantitative analysis and
might confound interpretation and misguide clinical decision-making. The present work …

Recent advancements in computational intelligence, deep learning, and computer-aided
detection have had a significant impact on the field of medical imaging. The task of image …