64 posts found
This cycle, the RSE team dedicated significant efforts to support various MICCAI 2023 challenges. Additionally, we enhanced the Cirrus viewer's usability, enabling seamless switching between different hanging protocols within the viewer. Furthermore, we addressed a prior issue, reinstating the functionality to switch between the polygon and mask editor. In the context of mask creation, we introduced a new feature allowing you to conveniently hide the annotation you're editing for accuracy verification using a straightforward keybinding. Explore our latest blog post for more details!
This cycle the RSE team has started working on a new, experimental feature: Implementing a client-side viewer, for now specifically for pathology images. This should provide a faster, more responsive experience when viewing these images. Furthermore, the GitHub repository integration has been improved and bugs squashed. Challenge organizers can now combine results from different phases to create an overall leaderboard. Finally, we have added support for viewing overlapping semantic segmentations.
30 May 2023 by Miriam Groeneveld
This cycle, the RSE team has made improvements for pathology. It is now possible to upload DICOM-WSI files, they will be converted to tiff. Segmentations as tiff files are now supported and will be validated according to the segmentation interface. The annotation statistics plugin has had an overall make-over, with progress bar and caching improving general usability. Other features include a 3D brush for creating and editing masks, and improvements to the admin page for challenge organizers.
As the first rays of spring sunshine warm our faces and the flowers begin to bloom, we are excited to bring you the latest news on the grand challenge ahead.
Cycle report of the research-software engineers of March 2023, including: more statistics on Grand Challenge, updated algorithm-job permissions on Grand Challenge, upgrade the CIRRUS backbone MeVisLab version to v3.6, added a new reader-study workflow for accepting and rejecting findings in both CIRRUS and Grand Challenge.
15 Feb. 2023 by Miriam Groeneveld
This cycle work was done to make algorithm result viewing faster by reusing Cirrus sessions. We also made it possible to select existing images for trying out an Algorithm, creating a Display Set in Reader Studies and creating Archive Items.
In this year's first release, we added the option to reuse existing images to run an algorithm on or create an archive item or display set. Previously this was only possible with the python client. We extended our annotation types with the ellipse annotation, to be used in Reader studies or Algorithms. Under the hood, we are working on improving the viewing of pathology images, making them faster and more error-proof. To improve the reliability and ease of use of the GC-API client, we have added retries to certain requests in the client. This should reduce the number of errors that need to be handled by the user.
29 Nov. 2022 by Miriam Groeneveld
A new tool and some bug fixes for the viewer in this cycle, while 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.
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.
Hi all! With this newsletter we want to update you on all progress made for Grand Challenge: upcoming Challenges, interesting new Algorithms and Blogposts, leaderboards of finished Challenges and new features of Grand Challenge.
The MICCAI Special Interest Group (SIG) for Challenges is hosting a webinar on How to run a challenge? on Oct 25th, 2022 at 1 pm GMT. Bram van Ginneken, Kiran Vaidhya Venkadesh, and Anindo Saha will present how to use Grand Challenge for organizing high-profile challenges. Jake Albrecht from Sage will present tips for challenge organizers on how to define a successful community challenge. Join us! Free registration is available at https://bit.ly/BIAS1025
11 July 2022 by Kicky van Leeuwen
It’s time to celebrate! We have reached a milestone of 200 products on our platform AIforRadiology.com, the most comprehensive overview of CE marked artificial intelligence (AI) based products in radiology. Enough reason for giveaways!
Hi all! With this newsletter, we want to update you on all progress made for Grand Challenge: upcoming Challenges, interesting new Algorithms, Blogposts, leaderboards of finished Challenges, and new features of Grand Challenge.
27 May 2022 by Kicky van Leeuwen
It has been a year since the more stringent Medical Device Regulation (MDR) replaced the former Medical Device Directive (MDD). With two years left on the clock for the 'transition period', only 17% of the AI products for radiology have obtained clearance under the MDR so far. If the market continues at this pace, half of the current CE marked AI products for radiology will not comply in time. The likely consequence is that software will be taken off the market and can no longer be used clinically after May 2024. Does that mean users stop buying MDD compliant AI software? Read about it in this blog!
AIROGS Challenge Report: AI models can be used for glaucoma screening, but do they know when they cannot?
The rationale behind the Artificial Intelligence for RObust Glaucoma Screening (AIROGS) Challenge, an overview of its results and our experiences with Grand Challenge's new Type 2 challenges.
Hi all! With this newsletter we want to update you on all progress made for Grand Challenge: new features, interesting new blogposts and algorithms, upcoming challenges and the leaderboards of finished challenges.
Can AI predict breast cancer recurrence via automated quantification of tumor-infiltrating lymphocytes?
TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) breast cancer histopathology slides. With TIGER, we released a training set of 390 whole-slide images and a total award of $13,000 in AWS Credits, which will be awarded to the winning teams. Together with its participants, we aim to find the best AI-based solutions for automating the assessment of the TILs and produce a “TILs score” that can predict the recurrence of breast cancer.
17 Feb. 2022 by Kicky van Leeuwen
Barely anyone working in the field of AI in healthcare received a formal education on the combination of topics. Simply, because with emerging fields, there usually isn’t much formal education yet. However, by now, with the market maturing, more and more educational resources are becoming available for AI in healthcare specifically. Are you looking to bridge that gap between healthcare and AI? This is the place to start!
Last week, we opened STOIC2021: A COVID-19 AI challenge with 10,000 CT scans. Together with its participants, we aim to find the best solution for predicting who will develop severe COVID-19 from a chest CT scan. We will make the final solution easily accessible for everyone. In total, $20,000 in AWS Credits will be awarded to the winning teams.
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.
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.
How to build a global, scalable, low-latency, and secure machine learning medical imaging analysis platform on AWS
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.