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

Deep learning (DL) has shown remarkable success in various medical imaging data
analysis applications. However, it remains challenging for DL models to achieve good …

Purpose In clinical practice, medical image analysis has played a key role in disease
diagnosis. One of the important steps is to perform an accurate organ or tissue segmentation …

Medical image segmentation models often fail to generalize well when applied to new
datasets, hindering their usage in clinical practice. Existing random-convolution-based …

To improve the quality of magnetic resonance (MR) image edge segmentation, some
researchers applied additional edge labels to train the network to extract edge information …

Cardiovascular disease, primarily caused by atherosclerotic plaque formation, is a
significant health concern. The early detection of these plaques is crucial for targeted …

Accurate building extraction holds paramount importance in various applications such as
urbanization rate calculations, urban planning, and resource allocation. In response to the …

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

Accuratelysegmenting polyps from colonoscopy images is essential for diagnosing
colorectal cancer. Despite the tremendous success of the deep convolutional neural …

Medical images are commonly used to diagnose and screen various diseases. However,
medical images taken by operators with different skill levels have a considerable variation in …