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November 2022 Cycle Report

29 Nov. 2022 by Miriam Groeneveld

Only bug fixes for the viewer in this cycle, as most effort has gone to the development of Grand Challenge Connect, presented at RSNA! A lot of interesting and exciting developments, so make sure to check out the dedicated blogpost.

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Grand Challenge at RSNA 2022

23 Nov. 2022 by James Meakin

Radboudumc will be at RSNA to present the latest developments of Grand Challenge, building on Amazon HealthLake Imaging. Visit us for a hands-on demo at the AWS Booth (6758) between 27-30 November 2022 at McCormick Place in Chicago.

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October 2022 Cycle Report

1 Nov. 2022 by Miriam Groeneveld

A relatively small update as we are working hard on something new and exciting, which will be revealed next cycle!

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September 2022 Cycle Report

26 Sept. 2022 by Miriam Groeneveld

Highlights include the addition of an angle tool, a rotate tool for pathology images and displaying probability scores for annotations.

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August 2022 Cycle Report

22 Aug. 2022 by Miriam Groeneveld

Highlights include the option to send an algorithm result to a reader study, using non-image type values in reader studies, the option to send feedback and more.

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June 2022 Cycle Report

8 Aug. 2022 by Miriam Groeneveld, Chris van Run, Harm van Zeeland, James Meakin, Paul K. Gerke and Anne Mickan

Highlights include improvements in type 2 challenge submission workflow, creating non-binary masks, adding static annotations to reader studies and more.

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May 2022 Cycle Report

5 July 2022 by Miriam Groeneveld, Harm van Zeeland and Chris van Run

Highlights include viewing a reader's answers, providing default answers for questions in a reader study, starting a reader study at a specific display set and more.

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How to build a global, scalable, low-latency, and secure machine learning medical imaging analysis platform on AWS

24 Aug. 2020 by Razvan Ionasec, Bram van Ginneken and James Meakin

It is hard to imagine the future for medical imaging without machine learning (ML) as its central innovation engine. Countless researchers, developers, start-ups, and larger enterprises are engaged in building, training, and deploying machine learning solutions for medical imaging that are posed to transform today’s medical workflows and the future value of imaging in diagnosis and treatment.

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