AI in cancer imaging
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AI Modeling
Integrative AI model building from large-scale imaging and non-imaging data.
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AI Assessment
Performance metrics/criteria and evaluation procedures
AI Modeling
EnCanImage is creating a collection of radiomics methods and AI algorithms for building novel integrative AI models from large-scale imaging and non-imaging data. They are offered through the AI Virtual Research Environment, a portable computational environment for supporting the development and validation of AI tools.
- Tools for cancer image feature extraction and selection
- Machine-learning pipeline for integrated predictive modelling
AI Development Platform
Run AI experiments
- Run EuCanImage AI tools on a private execution environment.
- Use the user-friendly web interface to upload your dataset or import them from any of the EuCanImage Data Repositories
- A pilot installation is online hosted at the Barcelona Supercomputing Center facilities.
AI assessment
EuCanimage consortium is developing tools, metrics and procedures for assessment of AI cancer-imaging methods in clinical environments. Aspects considered include model accuracy and reproducibility, model bias and uncertainty, as well as clinical effectiveness and usability.
OpenEBench
ELIXIR Benchmarking Platform
- Participate to benchmarking events organized by EuCanImage for assessing your AI method
- Inspect and visualize public benchmarking results
