Medical Image Quality Assurance (MIQA)
MIQA is designed for medical imaging QA/QC from the ground up, enabling workflows that not only reflect the specific requirements of distributed medical imaging studies, but also minimize the time spent on labor-intensive operations, such as visually reviewing scans.
Features
Multi-site
MIQA is cloud-based for distributed access by geographically distributed teams. All participants can securely view and annotate imagery from multiple sites.
AI Powered
MIQA provides neural networks pre-trained for anomaly detection to ease the burden of distributed quality assurance testing. MIQA will learn from annotations entered by experts to further improve its AI predictions.
Open Source
Open Source means MIQA can be extended and modified for new applications. Join our growing team of developers and develop only the extra features you need.
Modern UI/UX
MIQA uses new Javascript frameworks, including Vue.js, Vuetify, and Vuex to speed development and improve UI performance.
Efficient Data Management and Caching
MIQA builds on Girder, a mature, open source enterprise data hosting platform with multi-threading and scaleable storage and caching options.
Easy to Deploy
Get started right away using our pre-built docker containers.
Software we are utilizing
MIQA builds on open-source software to bring broad capabilities into our QA/QC platform
Our Collaborators
MIQA is brought to you by a collaborative team of medical imaging and software development experts, including
Aashish Chaudhary
Technical Leader
Data and Analytics, Kitware
OTHER TEAM MEMBERS
Matt McCormick
( Key Personnel )
Dženan Zukić
( Lead Developer )
Daniel Chiquito
( Lead Developer )
Scott Wittenberg
( Developer )
Jeff Baumes
( Administrative Support )
Kilian M. Pohl
Associate Professor of Psychiatry
Stanford
Program Director of Biomedical Computing
SRI International
Curtis Lisle
CEO
KnowledgeVis, LLC
Hans Johnson
Associate Professor
Electrical and Computer Engineering, IOWA
Support
The project is funded by the grant R44 MH119022 of the National Institute of Mental Health and Stanford Institute for Human-centered Artificial Intelligence (HAI) AWS Cloud Credit
