As artificial intelligence (AI) systems begin to make their way into clinical radiology practice,
it is crucial to assure that they function correctly and that they gain the trust of experts …

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 …

We propose a method for automatic segmentation of individual muscles from a clinical CT.
The method uses Bayesian convolutional neural networks with the U-Net architecture, using …

Purpose Surgical data science is a new research field that aims to observe all aspects of the
patient treatment process in order to provide the right assistance at the right time. Due to the …

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 …

Background: Artificial intelligence (AI) has recently achieved considerable success in
different domains including medical applications. Although current advances are expected …

Purpose The development of common ontologies has recently been identified as one of the
key challenges in the emerging field of surgical data science (SDS). However, past and …

Producing manual, pixel-accurate, image segmentation labels is tedious and time-
consuming. This is often a rate-limiting factor when large amounts of labeled images are …

Purpose For many applications in the field of computer-assisted surgery, such as providing
the position of a tumor, specifying the most probable tool required next by the surgeon or …

Social media platforms are facing increasing numbers of cyber-adversaries seeking to
manipulate online discourse by using social bots (ie, social media software robots) to help …