Deep graphical models and methods for multi-modal biomedical image processing, analysis, and interpretation

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Multi-modal imaging, where a multitude of imaging techniques are conducted in a single examination, has become an integral and crucial part of the modern healthcare system, as well as a powerful tool leveraged by research scientists to deepen the the understanding of diseases. However, due to the immense quantity as well as complexities of the acquired multi-modal imaging data, along with trade-offs between image quality and image acquisition times, there are significant challenges for clinicians and research scientists to interpret, understand, and analyse the acquired data in a semantically meaningful and efficient fashion. As such, novel methods for computer-aided processing, analysis, and interpretation of this wealth of multi-parametric imaging data can lead to significant improvements in not only disease screening and diagnosis, disease treatment planning and management, but also disease understanding.The main goal of the proposed research program is to develop novel computational models and intelligent algorithms for multi-modal biomedical image processing, analysis, and interpretation. Four main objectives will be investigated and explored: 1) deep graphical models and methods for improving the reconstruction and enhancement of acquired multi-modal imaging data, 2) novel deep graphical models for better characterizing the complex information captured in the acquired multi-modal imaging data, 3) deep model-driven image analysis for efficient and accurate extraction of quantitative information from a wealth of multi-modal imaging data in an explainable manner, and 4) deep model-driven artificial intelligence methods for efficient, accurate, and explainable computer-aided decision-making.The scientific and engineering results of the proposed research program will have a significant impact on the health and well-being of Canadians by improving disease screening and diagnosis, disease treatment planning and management, and improving disease understanding by providing new insights into the traits and mechanisms of disease through multi-modal imaging. The scientific knowledge and technologies developed during the proposed research program will be transferred into industry through active collaborations with companies such as Christie Medical, Agfa Healthcare, Hill-Rom Inc., and Elucid Labs. Furthermore, HQP will continue to be trained in image processing and analysis, computer vision, and artificial intelligence within a multi-disciplinary environment, putting them in a strong position for leadership roles in industry as well as in academia.