Outlier Detection for Foot Complaint Diagnosis: Modeling Confounding Factors Using Metric Learning

Abstract

Diagnosing foot complaints using plantar pressure videos is complicated by the presence of confounding factors (e.g., age, weight). Outlier detection could help with diagnosis, but these confounding factors result in data that are not independent and identically distributed (IID) with respect to a specific patient. To address this non-IID problem, we propose the modeling of confounding factors using metric learning. A distance metric is learned on the confounding factors in order to model their impact on the plantar pressures. This metric is then employed to weight plantar pressures from healthy controls when generating a patient-specific statistical baseline. Statistical parametric mapping is then used to compare the patient to this statistical baseline. We show that using metric learning reduces variance in these statistical baselines, which then improves the sensitivity of the outlier detection. These improvements in outlier detection get us one step closer to accurate computer-aided diagnosis of foot complaints.