Machine learning and decision support in critical care

AEW Johnson, MM Ghassemi, S Nemati… - Proceedings of the …, 2016 - ieeexplore.ieee.org
Clinical data management systems typically provide caregiver teams with useful information,
derived from large, sometimes highly heterogeneous, data sources that are often changing …

A survey on task assignment in crowdsourcing

D Hettiachchi, V Kostakos, J Goncalves - ACM Computing Surveys …, 2022 - dl.acm.org
Quality improvement methods are essential to gathering high-quality crowdsourced data,
both for research and industry applications. A popular and broadly applicable method is task …

COVID-19 government response event dataset (CoronaNet v. 1.0)

C Cheng, J Barceló, AS Hartnett, R Kubinec… - Nature human …, 2020 - nature.com
Governments worldwide have implemented countless policies in response to the COVID-19
pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 …

Image classification with deep learning in the presence of noisy labels: A survey

G Algan, I Ulusoy - Knowledge-Based Systems, 2021 - Elsevier
Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation

A Majkowska, S Mittal, DF Steiner, JJ Reicher… - Radiology, 2020 - pubs.rsna.org
Background Deep learning has the potential to augment the use of chest radiography in
clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty …

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

KL Hua, CH Hsu, SC Hidayati, WH Cheng… - OncoTargets and …, 2015 - Taylor & Francis
Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are
present. The management of small lung nodules noted on computed tomography scan is …

Learning from noisy labels by regularized estimation of annotator confusion

R Tanno, A Saeedi… - Proceedings of the …, 2019 - openaccess.thecvf.com
The predictive performance of supervised learning algorithms depends on the quality of
labels. In a typical label collection process, multiple annotators provide subjective noisy …

A survey on truth discovery

Y Li, J Gao, C Meng, Q Li, L Su, B Zhao… - ACM Sigkdd …, 2016 - dl.acm.org
Thanks to information explosion, data for the objects of interest can be collected from
increasingly more sources. However, for the same object, there usually exist conflicts among …

Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection

G Van Horn, S Branson, R Farrell… - Proceedings of the …, 2015 - openaccess.thecvf.com
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 …

Crowdsourced data management: A survey

G Li, J Wang, Y Zheng… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Any important data management and analytics tasks cannot be completely addressed by
automated processes. These tasks, such as entity resolution, sentiment analysis, and image …