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Frederick National Laboratory for Cancer Research issued a request for proposal. To further advance AI in Medical Imaging (AIMI) large datasets, acquired through routine standard of care, are needed to train and evaluate the performance of the ML/AI algorithms. The datasets need to be correctly de-identified to maintain patient privacy while at the same time preserving as much scientifically relevant information as possible. Large datasets from the existing standard of care radiology…



U.S. Department of Energy’s INCITE program seeks proposals for 2023: INCITE’s open call provides an opportunity for researchers to pursue transformational advances in science and technology through large allocations of computer time and supporting resources at the Argonne Leadership Computing Facility (ALCF) and the Oak Ridge Leadership Computing Facility (OLCF). Both are DOE Office of Science user facilities located at DOE’s Argonne…



Image result for images for cancer analyticsThe Second ECICC Community MicroLab on Cancer Challenges and Advanced Computing was held on September 25, 2019!

  • What is a MicroLab? MicroLabs are 60-90 minute, highly interactive, virtual events. Unlike webinars which are focused on disseminating information, the purpose of MicroLabs is to facilitate stimulating scientific discussions in smaller more intimate virtual breakout groups.

  • A multi-disciplinary group of over 100 clinicians, researchers, and academics in cancer and computational sciences participated in our second virtual, ECICC Community MicroLab on September 25, 2019!

  • Building on the breakout discussions from the first MicroLab held in June 2019, participants developed use cases for real-life situations and then identified what research challenges need to be overcome. The use cases were based on various personae derived from the 4 cancer challenge areas developed at the Envisioning Computational Innovations for Cancer Challenges (ECICC) Scoping Meeting held in March 2019.


MicroLab Presentations:


Presenters Included (partial list):

  • MicroLab Origins

    • Emily Greenspan, National Cancer Institute

  • Generating Large-Scale Synthetic Data to protect Personally Identifiable Information

    • Nick Anderson, University of California, Davis

    • Bill Richards, Brigham And Women's Hospital / Harvard University

  • Using Machine Learning for Iterative Hypothesis Generation

    • Amber Simpson, Queen’s University 

  • Creating a Cancer Patient “Digital Twin” to optimize personalized treatment decision-making

    • Tina Hernandez-Boussard, Stanford University

    • Paul Macklin, Indiana University

  • Developing Adaptive Cancer Treatments targeting unique tumor characteristics and trajectories

    • John McPherson, University of California, Davis

  • Use Case Demonstration

    • Paul Macklin, Indiana University


If you are interested in learning more -- or joining -- this multi-disciplinary community, please contact 


Created by Malachi Greaves Last Modified Mon April 25, 2022 4:00 pm by Lynn Borkon