17 posts found

What's up in AI for Radiology - November 2021

30 Nov. 2021 by Kicky van Leeuwen

From double to single read+AI | Market overview in 8 minutes | Good Machine Learning Practice | welcoming DeepTrace and Qubiotech | and more

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What's up in AI for Radiology - October 2021

27 Oct. 2021 by Kicky van Leeuwen

What is AI worth? | Two type AI vendors: developers and delivers | Introducing ChestView by GLEAMER | welcoming AIRAmed | and more

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Is AI worth its money?

5 Oct. 2021 by Kicky van Leeuwen

In the application of AI in healthcare, there is still a major question to be answered: who is going to pay - and how much - for AI in healthcare? A health technology assessment may help to find out. We applied this method to assess AI for stroke. In this blog, we discuss the results and implications and make the model available for your own use.

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What's up in AI for Radiology - September 2021

28 Sept. 2021 by Kicky van Leeuwen

Season of collaborations | first blog | dementia AI compared | welcoming Motilent, Medis Medical Imaging, and AI4MedImaging | and more AI for Radiology news

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The origin story of AI for Radiology and a market summary

14 Sept. 2021 by Kicky van Leeuwen

The market of artificial intelligence (AI) software for radiology keeps growing which makes it difficult for pretty much anyone in the field to maintain oversight. That's where AI for Radiology comes in. Read more about what we aim to do, how we do it, and learn what products are on the market today.

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What's up in AI for Radiology - August 2021

7 Sept. 2021 by Kicky van Leeuwen

Reimbursement for AI | head-to-head TB study | Qure.ai launches qCT-Lung | welcoming SenseTime, Smart Soft Healthcare, AlgoMedica, and annalise.ai | and more AI for Radiology news

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What's up in AI for Radiology - June 2021

7 Sept. 2021 by Kicky van Leeuwen

How to create impact with AI | mdbrain detects brain aneurysms | Improved breast cancer detection | Welcoming Lucida Medical, BrainScan and MaxQ | and more in this newsletter!

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What's up in AI for Radiology - May 2021

7 Sept. 2021 by Kicky van Leeuwen

Impact of MDR | Avicenna.ai, NICO.LAB and Arterys launch new features | Videos to 'unbox' AI | Welcoming Deep01 Limited | and more

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What's up in AI for Radiology - April 2021

7 Sept. 2021 by Kicky van Leeuwen

Limited evidence of AI products | CE marks for Ultromics, NICO.LAB, and Quantib | WHO advises AI for tuberculosis screening | and more!

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What's up in AI for Radiology - March 2021

7 Sept. 2021 by Kicky van Leeuwen

Arterys launches Neuro AI | Quibim and Imbio receive FDA clearance | icometrix' portfolio expands | The MDR explained | AI: To buy or not to buy

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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, Jasper Van Der Graaf and Joeran Bosma

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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