use the work, but more importantly, we created visualizations that you can turn on and off through our pipeline that allow people with relevant clinical or neurological knowledge to interpret what our computational model is doing on the back end,” she explains. “We hope that helps computational scientists, neuroscientists, or clinicians who want to adopt and expand this work translate it into clinical understanding.” One of the most significant challenges was the scarcity of clinical data, a common issue in research involving rare diseases like Parkinson’s. Clinical datasets are typically small and imbalanced, with more healthy cases than diseased ones. Favour developed a novel statistical method for learningbased sample selection to address this. This method identifies the most valuable samples in any given class for training, oversampling them to achieve a balanced representation across all classes. “There were some existing methods that did sample selection either randomly or through synthetic oversampling, but we thought it’d be better to address this directly in a stratified way,” she tells us. “Making sure there’s equal representation of that strong sample bias across every class before applying an external method like random oversampling.” The method performed very well, surpassing existing approaches, including those that inspired Favour to take up the work in the first place, with up to29% improvement for area under the curve (AUC). The success can be attributed to a solution comprising various techniques rather than a linear approach of only visualization explainability or sample selection. Favour plans to expand the research by evaluating it on a larger dataset, the Parkinson’s Progression Markers Initiative (PPMI) database, and exploring new methods involving self-attention and semisupervised techniques. 4 DAILY MICCAI Tuesday Oral Presentation
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