The overall aim of WP1 is to provide project management support to the consortium as a whole as well as to the individual consortium partners in matters linked to AI4Life. Professional project management will establish the conditions for effective and efficient delivery of AI4Life.

One of our major goals is to provide a comprehensive resource website for users and enable non-computational users from the life sciences to easily test-run and evaluate pre-trained models and run computational tools in the web browsers. We will provide a user-friendly UI, a cloud computing infrastructure for running AI models in the web browser and a standard for running tools in our computing infrastructure or connecting web applications and desktop software tools to the website. The computing infrastructure will be made available through providers within the European Open Science Cloud (EOSC).

The overall aim of the WP3 is to make the BioImage Model Zoo realise its full potential by bringing its Deep Learning (DL) tools and the related material to the point that they are easily and conveniently usable by the end-users and the model contributors. Without responsive and efficient user support, and good feedback to the consumer software, the BioImage Model Zoo risks becoming a container of models, data and code lacking real application. On the contrary, by implementing the AI4Life vision, we will achieve widespread use of DL tools in the life sciences community elevating the biological questions that can be answered to the next level. For that, WP3 will focus on consolidating the integration of the DL tools provided by the BioImage Model Zoo into the users’ image processing workflows. Moreover, the necessary mechanisms will be established to assure the correct deployment of the provided DL tools in real applications. We plan to achieve it through direct support for users and contributors of models.

WP4 aims to establish a framework that helps method developers and advanced image analysts to integrate their deep-learning-based algorithms into the AI4Life projects, such as the Bioimage Model Zoo. By doing so, we will empower developers with analytical and computational tools to reach a large audience of non-computational scientists and further their research capacity through accessible machine-learning technology. The WP, in particular, will provide base tools and guidelines, encouraging open contributions crowdsourced by the imaging community in a Findability, Accessibility, Interoperability, and Reusable (FAIR) form.