AI Assessment
Browse cancer datasets available through the platform.
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.
Access to Services or Software
OpenEBench
ELIXIR Benchmarking Platform
- Participate to benchmarking events organized by EuCanImage for assessing your AI method
- Inspect and visualize public benchmarking results.
In silico Trial Platform
In silico Validation
In silico platform allows to conduct studies to evaluate the added value of AI in the simulated clinical workflow. Three types of evaluations are possible:
- Clinicians without AI
- Clinicians with AI
- Clinicians with AI and Explainability
Radiomics Quality Score 2.0
Benchmarking Radiomics Studies
- Radiomics Quality Score 2.0 enables benchmarking deep learning and handcrafted radiomics research.
- The Radiomics Readiness Levels (RRLs) framework is embedded within RQS 2.0 to establish a structured, step-by-step approach to radiomics research.
Collective Minds Research
Collective Segmentation for Medical Imaging
An advanced, collaborative platform for precise medical image segmentation and other clinical research workflows, designed to accelerate AI-driven oncology research. Built on a secure, cloud infrastructure, it enables the collection, annotation, and benchmarking of cancer-related imaging multi-modal datasets, ensuring high-quality, GDPR-compliant workflows.
Collective Minds Connect
Automatic Imaging Data Collection, Pseudonymization, Tracking and Transfer
A seamless hospital edge gateway and data pipeline for automated imaging acquisition, anonymization, tracking and secure transfer. This service streamlines the entire lifecycle—from data ingestion through tracking to delivery—ensuring compliant, efficient, and traceable data transfers that facilitate federated AI development and multi-modal, multi-centric collaboration.
Benchmarking Challenges
Metrics and scores
| Detection Metrics |
|---|
| Intersection Over Union (IoU) |
| Boundary Intersection Over Union (Uncertainity Aware) |
| False Positives Per Image |
| FROC Curve (AUC-FROC) |
| Average Precision at various thresholds (alpha= 0.1 to 0.75) |
| Sensitivity at various thresholds (alpha = 0.1 to 0.75) |
| Classification Metrics |
|---|
| TPR/Sensitivity/Recall |
| TNR/Specificity |
| PPV/Precision |
| NPV |
| Accuracy |
| F1 Score |
| Balanced Accuracy |
| Cohen's Kappa |
| Weighted Cohen's Kappa |
| Mathews Correlation Coefficient |
| AUC Receiver Operating Characteristic Curve |
| AUC Precision Recall Curve |
| Segmentation Metrics |
|---|
| Dice Index |
| Surface Dice Index |
| Jaccard Index |
| Hausdorff Distance |
| Hausdorff Distance 95 percentile |
| Average Symmetric Surface Distance |
| Normalized Surface Distance |
| Modified Hausdorff Distance |
| Average Distance (2D) |
Datasets
(Controlled) access to datasets we expect to produce. Right now these resources are not yet created, but if we can compile the list of expected assets, I’ll create a table outlining them:
| Dataset Name | Type | Private XNAT/EGA link | |
|---|---|---|---|
| UC1 | Image testing Dataset | Not available yet | |
| Ground Truth | Not available yet | ||
| UC2 | Image testing Dataset | Not available yet | |
| Ground Truth | Not available yet | ||
| UC3 | Image testing Dataset | Not available yet | |
| Ground Truth | Not available yet | ||
| UC4 | Image testing Dataset | Not available yet | |
| Ground Truth | Not available yet | ||
| UC5 | Image testing Dataset | Not available yet | |
| Ground Truth | Not available yet | ||
| UC6 | Image testing Dataset | Not available yet | |
| Ground Truth | Not available yet | ||
| UC7 | Image testing Dataset | Not available yet | |
| Ground Truth | Not available yet | ||
| UC8 | Image testing Dataset | Not available yet | |
| Ground Truth | Not available yet | ||
Other sections or suggestions?
Please, let us know if you think we can include any other kind of information it would be valuable to share at the portal for AI data researchers willing to understand/use our AI assessment efforts ...