Biomedical image analysis algorithm validation depends on high-quality annotation of
reference datasets, for which labelling instructions are key. Despite their importance, their …

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 …

In many research areas, scientific progress is accelerated by multidisciplinary access to
image data and their interdisciplinary annotation. However, keeping track of these …

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 …

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

Deep-learning algorithms typically fall within the domain of supervised artificial intelligence
and are designed to “learn” from annotated data. Deep-learning models require large …

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 …

Abstract Machine learning has been widely adopted for medical image analysis in recent
years given its promising performance in image segmentation and classification tasks. As a …

Non-expert annotators (who lack sufficient domain knowledge) are often recruited for
manual image labeling tasks owing to the lack of expert annotators. In such a case, label …