Annotation and labeling for the development of AI models as well as non‑AI research projects

Get feedback on model predictions from your medical experts throughout a project's lifetime

Collaboration between stakeholders – medical experts, AI researchers, annotators, reviewers, project leads ...

Data privacy requirements – MVAI.ai runs entirely on-site, no data leaves your institution! Alternatively, MVA.ai can run on any cloud server or be hosted for you

Research consortia – easily collaborate across multiple sites, distribute the annotation / review process and manage your research project

Organize your project into annotation, review, correction ...

Assign different stakeholders to each node

Reviews using customizable UI forms per slice/volume

Eye-tracking – obtain 2D and 3D (!) heatmaps of what experts pay attention to while assessing medical data

Model Zoo – Segment and classify your image data using state of the art open-source AI models

Train your own AI models directly within MVA.ai

Active learning / Integration with Monai Label – annotate only the data that will actually improve your AI model

Additional modalities – work with µCT, 2D microscopy slides and large 3D microscopy data