Learn how we're thinking about benchmarking!
At CZI, we are interested in providing the infrastructure and methods support to advance biology-centric benchmarking, therefore bridging the gap between model developers and model users and advancing AI in biology. Read about a few of our initial collaborations and projects in this domain!
CZI Jamboree Launches Working Group for Single-Cell BenchmarkingNew
Learn about our recent jamboree and how it led to a new working group dedicated to benchmarking single-cell transcriptomic methods.
Read MoreEvaluating SubCell and Related Imaging ModelsNew
Overview of benchmarking tasks and challenges for evaluating deep learning models for single-cell fluorescence microscopy images.
Insights from the CZI-hosted Benchmarking and Evaluation Workshop
Learn about insights from a recent CZI workshop on building robust benchmarking for AI in biology
A Framework for Identifying, Assessing and Mitigating Biological Bias for AI in Biology
A compilation of resources that offer a framework for identifying and addressing biases in AI models for biology.
Designing a ML Competition for CryoET Data with Limited Annotations
Take a behind-the-scenes look at Chan Zuckerberg Imaging Institute's efforts in the development and hosting of an ML competition to boost cryo-electron tomography particle detection.