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Interpretable Machine Learning Models for the Digital Clock Drawing Test
The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions.
Balancing Appearance and Context in Sketch Interpretation
We describe a sketch interpretation system that detects and classifies clock numerals created by subjects taking the Clock Drawing Test, a clinical tool widely used to screen for cognitive impairments (e. g., dementia).
Action Recognition by Hierarchical Sequence Summarization
We develop an efficient learning method to train our model and show that its complexity grows sublinearly with the size of the hierarchy.
Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition
We propose a new sketch recognition framework that combines a rich representation of low level visual appearance with a graphical model for capturing high level relationships between symbols.
