Integration of multimodal imaging for development of automated diagnosis decision support

Medical imaging techniques such as X-rays, CT, and MRI generate large amounts of data that form the basis of radiomics, that allows the extraction of qualitative and quantitative information not directly available from clinical doctors. Recent research aims at creating innovative image and data fusion techniques.

The research will focus on the integration of standardized -omics and imaging data, and analytic software components to support medical diagnosis. This platform will be based on a sophisticated system design to meet clinicians’ requirements and support customization of the analysis pipeline, while at the same time providing an adequate usability in clinical practice.

The research will contribute to the development of a software platform that stakeholders of the regional health system can exploit for different types of omics data (genomics, transcriptomics, radiomics) and integrate them in order to create comprehensive models of the pathology of interest. The platform will provide tools for classification, interpretation, visualization, and reporting as well as AI-based methods for the identification of diagnostic, prognostic, and predictive biomarkers.

The main activities that will be carried out during the research project are:

1. Improve the usability of images generated by different imaging techniques, making it easier the visual analysis of complex data by clinicians

2. Creation of a prototype of an integrated visualization system for displaying 3D multimodal images, deriving from different technologies, where both conventional and -omics and decision cues superimposed with the images.

3. Development of novel computational strategies based on AI for exploiting -omics data for early diagnosis

Applications should be sent online following instructions at the following link: https://www.unifi.it/p12246.html (deadline 10/11/2022, h12 Italian Time).

The position is funded by the Minister of Research and University (MUR) under the PNRR program.

For detailed information on the project, please contact Prof. Leonardo Bocchi (leonardo.bocchi@unifi.it)