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The Medical Imaging Data Resource Center (MIDRC)


The Medical Imaging Data Resource Center (MIDRC) is an open data and open-source platform, offering researchers and medical professionals’ access to a diverse range of imaging data and resources in the field of medical imaging. MIDRC is a collaborative effort initiated in 2020 to tackle the global COVID-19 crisis. Funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and hosted at the University of Chicago and powered by the Gen3 platform, MIDRC is co-led by the American College of Radiology® (ACR®), the Radiological Society of North America (RSNA), and the American Association of Physicists in Medicine (AAPM). MIDRC is also now funded by ARPA-H and NAIRR as it pivots to oncology and other diseases/conditions continuing to contribute to the nation’s AI ecosystem.
Diverse Data Repository ( MIDRC hosts a vast collection of medical imaging datasets encompassing various modalities such as CT scans, X-rays, MRIs, and ultrasound images. These datasets are sourced from various institutions ensuring researchers have access to diverse and relevant data. MIDRC leverages OHIF's Viewer to facilitate the visualization of imaging datasets, enabling researchers and clinicians to interpret and extract insights from medical images more effectively. We highlight here a few projects with the help of tools and resources from OHIF.
Annotation and Labeling Tools: MIDRC has publicly-available algorithms on GitHub that are open and accessible to researchers. They democratize access to computational tools, fostering inclusivity and innovation. Additionally, they support education and skill development while facilitating community engagement and peer review, thereby advancing scientific knowledge and societal impact. We have highlighted a project with the help of tools and resources from OHIF,
MIDRC-MetricTree: This decision-tree-based tool is tailored to recommend performance metrics for computational research. This tool serves as a comprehensive guide for researchers navigating the multifaceted realm of performance evaluation in computational science. By following the branches of the tree, users can explore a range of parameters, measurements, and methodologies crucial for assessing the effectiveness and accuracy of algorithms.
MIDRC Bias awareness tool: This interactive decision tree is designed to assist researchers and practitioners in recognizing and addressing bias within computational materials science and related domains and addresses critical issues of bias in artificial intelligence (AI) and machine learning (ML) models.
Educational Materials: MIDRC provides educational resources such as tutorials, seminars, and support for researchers and medical professionals in utilizing the platform effectively. You can access these materials on our YouTube page, stay updated with our Newsletter, and follow us on LinkedIn and Twitter for the latest news and announcements.
Quality Assurance and Standards: MIDRC implements rigorous quality assurance measures to ensure the accuracy, reliability, and consistency of the imaging data hosted on the platform along with its customized LOINC mapping for data harmonization. Additionally, the platform adheres to industry standards and best practices in data management and analysis, providing users with confidence in the integrity of the data.
Collaborative Workspace: MIDRC provides a collaborative workspace where researchers and clinicians come together to collaborate, share insights, and work together on projects related to medical imaging analysis. We currently have 12 Collaborative Research Projects (CRPs) and 5 Technology Development Projects (TDPs) lead by members of the RSNA, ACR®, AAPM, and Gen3.
In conclusion, MIDRC is at the forefront of medical imaging, providing a wealth of open-access resources and tools for researchers and medical professionals globally. With its extensive data repository, resources, and cutting-edge technologies like OHIF's Viewer, MIDRC empowers users to drive progress in medical imaging research and practice.