Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

In the field of computer-and robot-assisted minimally invasive surgery, enormous progress
has been made in recent years based on the recognition of surgical instruments in …

Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally
invasive surgeries (MIS) represents a significant advancement in surgical technology. This …

Medical visual question answering (VQA) bridges the gap between visual information and
clinical decision-making, enabling doctors to extract understanding from clinical images and …

While semantic segmentation has seen tremendous improvements in the past, there are still
significant labeling efforts necessary and the problem of limited generalization to classes …

A common problem with segmentation of medical images using neural networks is the
difficulty to obtain a significant number of pixel-level annotated data for training. To address …

Surgical instrument segmentation is fundamentally important for facilitating cognitive
intelligence in robot-assisted surgery. Although existing methods have achieved accurate …

Automatic surgical instrument segmentation of endoscopic images is a crucial building block
of many computer-assistance applications for minimally invasive surgery. So far, state-of-the …

In conventional robot-assisted minimally invasive procedures (RMIS), surgeons have narrow
visual and complex working spaces, along with specular reflection, blood, camera-lens …

This paper is a comprehensive survey of datasets for surgical tool detection and related
surgical data science and machine learning techniques and algorithms. The survey offers a …