Data science is likely to lead to major changes in cardiovascular imaging. Problems with
timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The …

Abstract Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial
coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less …

Artificial intelligence is a broad field that comprises a wide range of techniques, where deep
learning is presently the one with the most impact. Moreover, the medical field is an area …

Abstract 3D bioprinting, a vital tool in tissue engineering, drug testing, and disease
modeling, is increasingly integrated with machine learning (ML) and artificial intelligence …

In single-image super-resolution (SISR) tasks, many methods benefit from the local and
global contexts of the image. Despite that, no methods use the bidirectional interaction …

Artificial intelligence (AI) promises to revolutionize many fields, but its clinical
implementation in cardiovascular imaging is still rare despite increasing research. We …

Data analysis methods have scarcely kept pace with the rapid increase in Earth
observations, spurring the development of novel algorithms, storage methods, and …

Image resizing is frequently used as a preprocessing step in many computer vision tasks,
especially in medical applications. While tuning of the resizing method is usually omitted in …

Accurately predicting the impact of hormonal therapy on Prostate Cancer (PC) lesions is
paramount for effective treatment planning and monitoring. This study proposes a …

The aptness of machine learning (ML) to learn from large datasets, discover trends, and
make predictions has demonstrated its potential to metamorphose the medical field. Medical …