36 MICCAI Oral Presentation “We also want to do something that I’mreally passionate about, which is trying to identify cross-modal relationships between our various features,” she reveals. “In this work, we focus on the neuroimaging side, taking the rs-fMRI connectivity matrices and then optimizing that using Riemannian geometry and leveraging some features. Now, I’m interested in combining some of the patient attributes and seeing how that could better inform linkages that could be learned during training amongst other types of techniques we’ll try.” Favour has several follow-up projects in the pipeline that promise to push the boundaries further, leveraging attention-based methods and geometric deep learning techniques. Beyond neuroimaging, she aims to incorporate video data from the same patients. This multimodal approach is a significant next step. She intends to derive specific motion biomarkers that can be associated with the existing features. This expansion aims to optimize the learning process and further enhance the understanding of those linkages. The ultimate goal is to combine all these modalities into a comprehensive framework that can be generalized to a broader population. She envisions creating a foundation model that can serve as a valuable resource for researchers and clinicians in various downstream tasks. BEST OF MICCAI 2023
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