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

Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges
such as scalability and acceptance. These challenges include data availability, biased …

Abstract Computer Aided Diagnosis (CAD) systems are adopting advancements at the
forefront of computer vision and machine learning towards assisting medical experts with …

Inference of clinically-relevant findings from the visual appearance of images has become
an essential part of processing pipelines for many problems in medical imaging. Typically, a …

Capsule endoscopy (CE) is a valid alternative to conventional gastrointestinal (GI)
endoscopy tools. In CE, annotation tools are crucial in developing large and annotated …

Deep-learning-based pipelines have shown the potential to revolutionalize microscopy
image diagnostics by providing visual augmentations and evaluations to a trained pathology …

Sharing radiologic image annotations among multiple institutions is important in many
clinical scenarios; however, interoperability is prevented because different vendors' PACS …

Purpose Annotation of surgical videos is a time-consuming task which requires specific
knowledge. In this paper, we present and evaluate a deep learning-based method that …

Goal: Most state-of-the-art computer-aided endoscopic diagnosis methods require pixelwise
labeled data to train various supervised machine learning models. However, it is a tedious …

The use of deep learning and other machine learning techniques requires large amounts of
annotated image data. There exist several tools to annotate images, however to our …