Introduction With increasing use of robotic surgical adjuncts, artificial intelligence and
augmented reality in neurosurgery, the automated analysis of digital images and videos …

Over the years, the paradigm of medical image analysis has shifted from manual expertise to
automated systems, often using deep learning (DL) systems. The performance of deep …

Due to the limited amount of available annotated data in the medical field, domain
generalization for applications in computer-assisted surgery is essential. Our work …

Integrating Minimally Invasive Surgery (MIS) with robotic systems has revolutionized surgical
procedures by enhancing precision, reducing patient discomfort, and shortening recovery …

Towards computer-assisted neurosurgery, scene understanding algorithms for microscope
video data are required. Previous work utilizes optical flow to extract spatiotemporal context …

Recent approaches for surgical activity localization rely on motion features derived from the
optical flow (OF). However, although they consider state-of-the-art CNNs when computing …

Abstract Machine learning-based solutions rely heavily on the quality and quantity of the
training data. In the medical domain, the main challenge is to acquire rich and diverse …

Deep learning-based computer vision applications in surgery tend to lack sufficiently rich
training data. This shortcoming leads to models that often perform well only in a limited …