Shapelet analysis of pupil dilation for modeling visuo-cognitive behavior in screening mammography

Our objective is to improve understanding of visuo-cognitive behavior in screening mammography under clinically equivalent experimental conditions. To this end, we examined pupillometric data, acquired using a head-mounted eye-tracking device, from 10 image readers (three breast-imaging radiologists and seven Radiology residents), and their corresponding diagnostic decisions for 100 screening mammograms. The corpus of mammograms comprised cases of varied pathology and breast parenchymal density. We investigated the relationship between pupillometric fluctuations, experienced by an image reader during mammographic screening, indicative of changes in mental workload, the pathological characteristics of a mammographic case, and the image readers’ diagnostic decision and overall task performance. To answer these questions, we extract features from pupillometric data, and additionally applied time series shapelet analysis to extract discriminative patterns in changes in pupil dilation. Our results show that pupillometric measures are adequate predictors of mammographic case pathology, and image readers’ diagnostic decision and performance with an average accuracy of 80%.