Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

Despite learning based methods showing promising results in single view depth estimation
and visual odometry, most existing approaches treat the tasks in a supervised manner …

Self-supervised learning methods have witnessed a recent surge of interest after proving
successful in multiple application fields. In this work, we leverage these techniques, and we …

Deep learning techniques hold promise to develop dense topography reconstruction and
pose estimation methods for endoscopic videos. However, currently available datasets do …

Robotic-assisted surgery is now well established in clinical practice and has become the
gold-standard clinical treatment option for several clinical indications. The field of robotic …

Many applications of stereo depth estimation in robotics require the generation of accurate
disparity maps in real time under significant computational constraints. Current state-of-the …

This paper proposes a self-supervised learning method for the person re-identification (re-
ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as …

Artificial intelligence (AI) is gradually changing the practice of surgery with technological
advancements in imaging, navigation, and robotic intervention. In this article, we review the …