The use of digital twins (DTs) has proliferated across various fields and industries, with a
recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds …

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the
same drugs or surgical methods in patients with the same tumor may have different curative …

Radiomics is a process that allows the extraction and analysis of quantitative data from
medical images. It is an evolving field of research with many potential applications in …

Advancements in data acquisition and computational methods are generating a large
amount of heterogeneous biomedical data from diagnostic domains such as clinical …

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to
bring medicine from the era of 'sick-care'to the era of healthcare and prevention. The …

The use of artificial intelligence (AI) with medical images to solve clinical problems is
becoming increasingly common, and the development of new AI solutions is leading to more …

Simple Summary Tumor segmentation is a key step in oncologic imaging processing and is
a time-consuming process usually performed manually by radiologists. To facilitate it, there …

Background Estimating the required sample size is crucial when develo** and validating
clinical prediction models. However, there is no consensus about how to determine the …

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a
method to save time, cost and improve efficiencies. The high-performance statistics and …

The CHAIMELEON project aims to set up a pan-European repository of health imaging data,
tools and methodologies, with the ambition to set a standard and provide resources for …