7 posts found

AI challenges, data, and algorithms

4 March 2021 by Bram van Ginneken

Presentation by Bram van Ginneken held during the European Congress of Radiology 2021 on the how and why of challenges in medical image analysis.

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Why Challenges?

14 Feb. 2021 by Keelin Murphy and Bram van Ginneken

This is an updated version of blog post made years ago when we launched our platform grand-challenge.org. It makes the case for challenges and provides some advice for those interested in setting up their own challenge.

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Create an Algorithm container

11 Feb. 2021 by Kiran Vaidhya Venkadesh, James Meakin, Bram van Ginneken, Khrystyna Faryna and Jasper Van Der Graaf

This blog post describes how you can encapsulate your algorithm in a Docker container and upload it to grand-challenge.org

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Create a Reader Study

10 Feb. 2021 by Jasper Van Der Graaf, Kiran Vaidhya Venkadesh and Leslie Tessier

This blog shows you how to create a Reader Study in grand-challenge.org

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Create Your Own Challenge

21 Jan. 2021 by Kiran Vaidhya Venkadesh, Khrystyna Faryna, James Meakin, Bram van Ginneken and Ecem sogancioglu

This blog post provides instructions for creating your own challenge on grand-challenge.org along with videos of an example challenge.

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Visualisations For Challenges

6 Nov. 2020 by James Meakin, Adriënne Mendrik, Maarten van Meersbergen, J.G. Gonzalez and Berend Weel

Visualizations of algorithm results beyond the challenge leaderboard can aid in gaining insight into algorithm performance for a specific task and in finding new research directions. grand-challenge.org offers an option to add visualizations to your challenge through ObservableHQ notebooks. You can use Vega or Vega-lite to easily create graphs and the Vega Editor to edit them, before integrating them in your ObservableHQ notebook that will be embedded in your challenge page on grand-challenge.org. To get you started, we provided a couple of example notebooks for different types of challenges (e.g. a classification, segmentation and detection challenge).

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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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