The Medical image Network Enterprise (MiNE) is a scalable, community-defined e-infrastructure with the primary objective of housing an electronic image-based inventory and data warehouse. MiNE also provides related tools for research and education, to support and encourage the University of Toronto research community. MiNE advocates the efficient use and privacy-sensitive sharing of existing research and clinical image data.


Bridging the gap between clinical expertise and the science of managing and analyzing medical imaging data is challenging. To provide direction for data management as well as the analysis and reporting of research findings, the Department of Medical Imaging at the University of Toronto has introduced a data science unit – MiDATA – offering users an environment geared towards a “soup to nuts” approach to medical imaging research methodology, statistics, and machine learning (MiDATA).

The aim of this proposal is to: 1) establish a research agreement and network between the Costa Rican healthcare system and Canadian medical imaging research (MiDATA – UofT) to share and transfer data and knowledge; 2) create a research environment and develop the methodological pipeline and associated tools required for transformative AI solutions for clinical applications involving medical imaging.

In partnership with the Costa Rican healthcare system, local Costa Rican universities (TEC) and tech industry (QXD), we propose to leverage existing Canadian e-infrastructure: medical image network enterprise (MiNE), which is a seamless connection of a medical imaging research ‘pipeline’ to the clinical stream with images, structured reports, post-processing measurements and clinical data. The MiNE project is also partnered with industry (IBM Watson Health and the AI company 16Bit) and is currently establishing a link with the smart computing for innovation (SOSCIP) research and development consortium. An innovative shared AI framework would enable the more rapid development and implementation of medical AI solutions by connecting leaders in machine intelligence with researchers, clinicians, patients and families rapidly and thereby informing effective care decisions, advancing safety of care and decreasing health care expenditures in both countries.


MiDATA’s supporting the development of AI solutions through the launch of the MiStats+ML working group. The MiStats+ML working group will support all department medical image-based research in all relevant domains.

The MiStats+ML working group represents the ideal blend of insight and expertise from the domains of statistics, medical imaging, and bioinformatics (machine learning). The team is composed of three aspects representing medical imaging, machine learning and statistical analysis:

1. Medical Imaging

  • Develops and tests experimental planning and image analysis tools as related to the investigation of developmental disorders and disease prevention.

Current People: Atsuhiro Hibi

2. Machine Learning

  • Assesses measurement reliability, conducts large-scale medical imaging analyses for the development of clinical diagnostic tests, such as ultrasound medical imaging and synovium inflammation.
  • Documentation through ADLC with compliance with regulatory requirements.
  • Designs and trains image classification and regression algorithms.
  • MiStats+ML is partnered with 16Bit for education, consultation, and commercialization.

Current People: Dmitrii Paniukov, Fatma Eltawil, Lee Radigan, and Rajshree Daulatabad

3. Research Methodology and Statistical Analysis

  • Investigates finite mixture models, clustering and classification.
  • Tests dynamic systems, applies simulations, numerical, topological and functional data analyses.
  • Current research entails the application in which uncertainty measures are applied alongside the prediction of a standard CNN classifier for medical imaging, to incorporate a degree of confidence into the workflow. 

Current People: John Valen, Mauro Mendez, Pascal Tyrell, Indranil Balki, and Wendi Qu


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