Background and study aims The contribution of artificial intelligence (AI) to endoscopy is
rapidly expanding. Accurate labelling of source data (video frames) remains the rate-limiting …

Several computer-based medical systems have been proposed for automatic detection of
abnormalities in a variety of medical imaging domains. The majority of these systems are …

In this work, we present a novel approach to generate large amounts of training data for
supervised machine learning algorithms. Traditionally, labeling a high quantity of data …

Background Machine learning, especially deep learning, is becoming more and more
relevant in research and development in the medical domain. For all the supervised deep …

Purpose: In medical research, deep learning models rely on high-quality annotated data, a
process often laborious and time-consuming. This is particularly true for detection tasks …

Today gastroenterologists face the challenge of how to perceive and interpret the high
volume—about 30 highdefinition frames per s—of rich visual data presented in real time …

With the recent breakthrough success of machine learning based solutions for automatic
image annotation, the availability of reference image annotations for algorithm training is …

Background Artificial intelligence (AI) holds tremendous potential to reduce surgical risks
and improve surgical assessment. Machine learning, a subfield of AI, can be used to …

Medical imaging refers to several different technologies that are used to view the human
body to diagnose, monitor, or treat medical conditions. It requires significant expertise to …

The lack of annotated datasets is a major bottleneck for training new task-specific
supervised machine learning models, considering that manual annotation is extremely …