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).
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.