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

The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of
humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the …

Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Most of the existing FSS techniques require abundant annotated semantic classes for …

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 …

Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …

Fully-supervised deep learning segmentation models are inflexible when encountering new
unseen semantic classes and their fine-tuning often requires significant amounts of …

Deep learning-based medical image segmentation models often suffer from domain shift,
where the models trained on a source domain do not generalize well to other unseen …

The goal of cross-view image matching based on geo-localization is to determine the
location of a given ground-view image (front view) by matching it with a group of satellite …

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the
security and robustness of the deployed algorithms need to be guaranteed. The security …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …