Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …

Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …

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

Deep learning techniques for 3D brain vessel image segmentation have not been as
successful as in the segmentation of other organs and tissues. This can be explained by two …

Rapid advances in image processing capabilities have been seen across many domains,
fostered by the application of machine learning algorithms to" big-data". However, within the …

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

Background and objective: Lung cancer is the most common type of cancer with a high
mortality rate. Early detection using medical imaging is critically important for the long-term …

Rapid advances in image processing capabilities have been seen across many domains,
fostered by the application of machine learning algorithms to" big-data". However, within the …

Classifiers for medical image analysis are often trained with a single consensus label, based
on combining labels given by experts or crowds. However, disagreement between …

This paper investigates what quality of ground truth might be obtained when crowdsourcing
specialist medical imaging ground truth from non-experts. Following basic tuition, 34 …