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Explainable AI for Visual Snow Syndrome

Primary supervisor

Leimin Tian


Visual Snow Syndrome (VSS) is a recently identified neurological disorder that the medical community still does not know much about. Diagnosing VSS is challenging and mostly reliant on subjective reports from patients. Existing studies demonstrated that VSS patients exhibit different behaviours compared to healthy controls in visual attention tasks, specifically pro-saccade, anti-saccade, and pro/anti switching tests. Using data collected at the Neurology department, pilot deep learning experiments have been conducted using the LSTM model with attention mechanism. These pilot experiments yield promising results indicating that automatic diagnosis of VSS based on a person’s visual attention task performance is possible, and the classifier can achieve performance on-par or even more accurate than human doctors.

    Student cohort

    Double Semester


    In this project, you will extend the pilot experiments with two objectives:

    1. Use explainable AI tools, such as LIME or InterpretML, to investigate the pilot LSTM model and understand what makes it so effective. This may lead to discoveries on the causes or treatments of VSS.
    2. Improve the pilot LSTM model if possible, and test other deep learning or machine learning models that may yield reliable classification performance.


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