A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the
potential for many fields—including medicine—to benefit from the insights that AI techniques …

This review examines the dichotomy between automatic and autonomous behaviors in
surgical robots, maps the possible levels of autonomy of these robots, and describes the …

Background and objectives Medical image analysis and computer-assisted intervention
problems are increasingly being addressed with deep-learning-based solutions …

One of the fundamental challenges in supervised learning for multimodal image registration
is the lack of ground-truth for voxel-level spatial correspondence. This work describes a …

Semantic segmentation of robotic instruments is an important problem for the robot-assisted
surgery. One of the main challenges is to correctly detect an instrument's position for the …

Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action
triplet recognition stands out as the only one aiming to provide truly fine-grained and …

The fields of computer vision (CV) and artificial intelligence (AI) have undergone rapid
advancements in the past decade, many of which have been applied to the analysis of …

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 …

In mainstream computer vision and machine learning, public datasets such as ImageNet,
COCO and KITTI have helped drive enormous improvements by enabling researchers to …

The Dice score is widely used for binary segmentation due to its robustness to class
imbalance. Soft generalisations of the Dice score allow it to be used as a loss function for …