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. Research journeys are rarely smooth sailing, and Favour tells us a remarkable aspect of this one was the need for constant course correction in her coding efforts. As the deadline for MICCAI submission approached, the results were not exactly where she needed them to be. “It was weighing on my heart so heavily,” she admits. “At first, we were even doing an approach of comparative binary classification and multi-class classification, and things just weren’t making sense. Then, I just focused on the multiclass classification. Once I did that and started to look into how I could directly optimize my metrics, ensuring everything was weighted in my loss functions, sampling techniques, and all those things, we started to see consistent results that could be repeated over trials. I was so concerned about that because I’d get good results here and there, but I couldn’t repeat them. I was so happy once it got stable enough to have reproducible results. That literally happened a few weeks before we were supposed to submit! I kept updating my paper every day until the night of submission.” Favour remains dedicated to her academic journey. With a couple of years to go until she completes her PhD, she is committed to ongoing research in the field and leadership roles both on and off campus. As we wrap up our time together, she acknowledges the importance of the support she has received along the way. This work has been a significant milestone as her first selfowned project and paper to be released during her graduate school career. It now has the honor of being accepted as an oral at a prestigious conference like MICCAI. 6 DAILY MICCAI Tuesday Oral Presentation
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