Clinical data management systems typically provide caregiver teams with useful information,
derived from large, sometimes highly heterogeneous, data sources that are often changing …

Purpose–The purpose of this paper is to explore the development of crowdsourcing
literature. Design/methodology/approach–This study is a comprehensive review of 346 …

Patterns and textures are key characteristics of many natural objects: a shirt can be striped,
the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at …

Learning with the\textit {instance-dependent} label noise is challenging, because it is hard to
model such real-world noise. Note that there are psychological and physiological evidences …

Distributing labeling tasks among hundreds or thousands of annotators is an increasingly
important method for annotating large datasets. We present a method for estimating the …

The sample selection approach is popular in learning with noisy labels. The state-of-the-art
methods train two deep networks simultaneously for sample selection, which aims to employ …

We introduce tools and methodologies to collect high quality, large scale fine-grained
computer vision datasets using citizen scientists--crowd annotators who are passionate and …

The predictive performance of supervised learning algorithms depends on the quality of
labels. In a typical label collection process, multiple annotators provide subjective noisy …

International challenges have become the standard for validation of biomedical image
analysis methods. Given their scientific impact, it is surprising that a critical analysis of …

Any important data management and analytics tasks cannot be completely addressed by
automated processes. These tasks, such as entity resolution, sentiment analysis, and image …