Abstract Machine learning and computer vision techniques have grown rapidly in recent
years due to their automation, suitability, and ability to generate astounding results. Hence …

Within medical imaging, organ/pathology segmentation models trained on current publicly
available and fully-annotated datasets usually do not well-represent the heterogeneous …

Fully convolutional networks (FCNs) trained with abundant labeled data have been proven
to be a powerful and efficient solution for medical image segmentation. However, FCNs …

Artificial intelligence (AI) generation in medical image synthesis provides a more accurate
and efficient method for medical image analysis. Medical image segmentation can assist …

Partially-supervised learning can be challenging for segmentation due to the lack of
supervision for unlabeled structures, and the methods directly applying fully-supervised …

Detecting vehicles is vital for traffic monitoring and surveillance which generally employ
cameras on bridges or roadsides. However, aerial imagery offers greater flexibility by …

Multi-modal medical image segmentation plays a vital role in clinical applications such as
auxiliary diagnosis and surgical planning. However, it is still a challenging task to extract …

Semantic segmentation is important in medical image analysis. Inspired by the strong ability
of traditional image analysis techniques in capturing shape priors and inter-subject …

The emerging research field of exercise immunology is becoming an essential sub-
discipline of exercise medicine. Many studies have revealed a strong association between …

Clinical practice significantly benefits from the precise delineation of medical images, which
aids in the precise identification of conditions, the formulation of therapeutic strategies, and …