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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…

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U.S. Department of Energy’s INCITE program seeks proposals for 2023: https://www.doeleadershipcomputing.org/. 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…

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U.S. Department of Energy’s INCITE program seeks proposals for 2023: https://www.doeleadershipcomputing.org/. 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 National Laboratory and Oak Ridge National Laboratory, respectively. The deadline to apply is June 17.

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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 practice, along with companion clinical data, are needed for the training and development of ML/AI algorithms by the research community. Offerors must contact Connor Cigrang, Subcontract Administrator, for the official RFP Document and Attachments. Interested vendors must advise the SCA of intention to bid no later than 5PM, May 23, 2022. All questions and requests for clarification are due by 5PM, May 23, 2022 and will be answered no later than 5PM, Tuesday, May 31, 2022. There will be no Offeror Teleconference.

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Attend Precision Medicine Applications in Radiation Oncology, a meeting sponsored by The Cancer informatics for Cancer Centers (Ci4CC), August 29–31, 2022. This in-person symposium in Santa Barbara, CA will feature invited talks on innovative applications of computational, quantitative, and machine learning approaches to enhance the precision of biomarker development, theranostics, decision support, and workflow in radiation oncology. There will also be a call for scientific abstracts that will be featured in a poster session.

Fall Registration Page

Current Meeting Abstract and Agenda

Ci4CC is a nonprofit society providing a focused forum for NCI Designated & Community Cancer Centers that has a special focus on Precision Medicine, Data Science, Artificial Intelligence, Healthcare IT, Translational Research, & Digital Platforms targeting Executive Informatics & Research IT leaders nationally.

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The Virtual Digital Twin Micro Lab was held on April 23, 2020 with participants from more than 40 organizations. Download the video presentations, see the PowerPoint slides and read the breakout discussion notes!

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Registration is now open for the 2019 ML-MSM Meeting (October 24-25, 2019) to be held in Bethesda, MD (NIH Campus). The meeting will focus on multiple domain approaches to developing Digital Twins and addressing Human Safety.

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Paul Macklin of Indiana University – and MicroLab co-lead of Digital Twin ― co-authored a paper with Argonne National Lab (which includes running many patient simulations on HPC to screen treatment choices). The paper is titled, “Learning-accelerated discovery of immune-tumour interactions,” published in Molecular Systems Design and Engineering on June 7, 2019.

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