Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …

Data augmentation involves artificially expanding a dataset by applying various
transformations to the existing data. Recent developments in deep learning have advanced …

Deep learning models usually suffer from the domain shift issue, where models trained on
one source domain do not generalize well to other unseen domains. In this work, we …

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) …

The domain gap caused mainly by variable medical image quality renders a major obstacle
on the path between training a segmentation model in the lab and applying the trained …

A high-performance computer-aided diagnosis (CAD) system can enhance the accuracy of
liver cancer diagnosis, enabling early detection, diagnosis, and treatment. However, the …

Convolutional neural networks (CNNs) have achieved remarkable segmentation accuracy
on benchmark datasets where training and test sets are from the same domain, yet their …

Cardiac magnetic resonance (CMR) image segmentation is an integral step in the analysis
of cardiac function and diagnosis of heart related diseases. While recent deep learning …

Recent advances in deep learning for brain tumor segmentation demonstrate good
performance when the training data and test data share the same distribution. However …

Training medical image segmentation models usually requires a large amount of labeled
data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from …