Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

The recent development in the areas of deep learning and deep convolutional neural
networks has significantly progressed and advanced the field of computer vision (CV) and …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

In order to achieve good performance and generalisability, medical image segmentation
models should be trained on sizeable datasets with sufficient variability. Due to ethics and …

Deep learning-based segmentation methods provide an effective and automated way for
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …

Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due
to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a …

It is critical to quantitatively analyse the develo** human fetal brain in order to fully
understand neurodevelopment in both normal fetuses and those with congenital disorders …

This paper explores the use of 'conditional convolutional generative adversarial
networks'(CDCGAN) for image-based leak detection and localization (LD&L) in water …

Deep-learning (DL) performs well in computer-vision and medical-imaging automated
decision-making applications. A bottleneck of DL stems from the large amount of labelled …

Brain segmentation is often the first and most critical step in quantitative analysis of the brain
for many clinical applications, including fetal imaging. Different aspects challenge the …