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 …

Generative models have been very successful over the years and have received significant
attention for synthetic data generation. As deep learning models are getting more and more …

One of the biggest issues facing the use of machine learning in medical imaging is the lack
of availability of large, labelled datasets. The annotation of medical images is not only …

Accurate brain tumour segmentation is critical for tasks such as surgical planning, diagnosis,
and analysis, with magnetic resonance imaging (MRI) being the preferred modality due to its …

Background Missing MRI sequences represent an obstacle in the development and use of
deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing …

Training segmentation networks requires large annotated datasets, but manual annotation is
time consuming and costly. We here investigate if the combination of a noise-to-image GAN …

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 …

In the recent past, deep learning-based models have achieved tremendous success in
computer vision-related tasks with the help of large-scale annotated datasets. An interesting …

Medical image analysis has significantly benefited from advancements in deep learning,
particularly in the application of Generative Adversarial Networks (GANs) for generating …

Computer-assisted diagnosis (CAD) based on deep learning has become a crucial
diagnostic technology in the medical industry, effectively improving diagnosis accuracy …