On the Within-class Variation Issue in Alzheimer's Disease Detection
The episode discusses a paper titled "On the Within-class Variation Issue in Alzheimer's Disease Detection." Hosts discuss how current diagnostic models fail by assuming patient homogeneity, suggesting instead that systems must map out a spectrum of potential decline. The proposed solution involves using multi-modal, longitudinal data to track individual change over time rather than relying on single snapshots.