InVisioning* Lab

InVisioning Lab develops methodologies and tools for quantitative biomedical imaging, with the aim of transforming complex biomedical images and signals into reliable, interpretable, and useful information for biomedical research and for the development of advanced healthcare solutions. The group brings together expertise in engineering, mathematics, and ICT to address the characterization of biological tissues in different contexts, combining data acquisition, image processing, mathematical modeling, and artificial intelligence.
The laboratory’s activities are framed within a multiscale and multimodal perspective. On the one hand, the group studies ex vivo images, such as high-resolution histological images, to quantitatively analyze the microstructure of biological tissues. In this area, advanced image processing and AI techniques make it possible to move from predominantly qualitative assessment to more objective, reproducible, and automated descriptions, reducing subjectivity in interpretation and increasing the robustness of analysis workflows.
On the other hand, the laboratory develops and applies in vivo quantitative imaging approaches, including ultrasound and photoacoustic imaging, to obtain non-invasive information on the structural and functional properties of tissues. These methodologies generate large amounts of heterogeneous data, whose interpretation requires mathematical models and algorithms capable of improving their quality, readability, and informative value.
A distinctive feature of InVisioning Lab is its use of AI not only as an automation tool, but also as a means of supporting the quantitative representation and understanding of biological phenomena. For this reason, the group pays particular attention to issues such as reliability, controllability, validation, and algorithm transparency, which are essential in the biomedical field. The goal is not only to achieve high-performing models, but also to build methods that are robust, verifiable, and interpretable.
By integrating data from different imaging modalities and observations at different scales, from tissue microstructure to the in vivo organism, InVisioning Lab aims to develop intelligent solutions capable of transforming the complexity of data into useful, actionable knowledge.