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

Accurate segmentations in medical images are the foundations for various clinical
applications. Advances in machine learning-based techniques show great potential for …

Accurate annotations of medical images are essential for various clinical applications. The
remarkable advances in machine learning, especially deep learning based techniques …

Abstract Machine learning algorithms are gaining increasing interest in the context of
computer-assisted interventions. One of the bottlenecks so far, however, has been the …

Computer-assisted minimally-invasive surgery (MIS) is often based on algorithms that
require establishing correspondences between endoscopic images. However, reference …

With the rapidly increasing interest in machine learning based solutions for automatic image
annotation, the availability of reference annotations for algorithm training is one of the major …

Crowdsourced demarcations of object boundaries in images (segmentations) are important
for many vision-based applications. A commonly reported challenge is that a large …

For medical image segmentation, deep learning approaches using convolutional neural
networks (CNNs) are currently superseding classical methods. For good accuracy, large …

State-of-the-art computer-vision algorithms rely on big and accurately annotated data, which
are expensive, laborious and time-consuming to generate. This task is even more …

Purpose: Methodology evaluation for decision support systems for health is a time-
consuming task. To assess performance of polyp detection methods in colonoscopy videos …