- Location
- Virtual over Zoom
- Series/Type
- DLSPH Event
- Dates
- February 1, 2022 from 3:00pm to 4:00pm
The Biostatistics Seminar Series presents:
“Significant Anatomy Detection Through Sparse Classification: A Comparative Study” by Dr. Linglong Kong, University of Alberta
Abstract: We present a comparative study for discriminative anatomy detection in high dimensional neuroimaging data. While most studies solve this problem using mass univariate approaches, recent works show better accuracy and variable selection using a sparse classification model. Two types of image-based regularization methods have been proposed in the literature based on either a Graph Net (GN) model or a total variation (TV) model. These studies showed increased classification accuracy and interpretability of results when using image-based regularization, but did not look at the accuracy and quality of the recovered significant regions. We theoretically prove bounds on the recovered sparse coefficients and the corresponding selected image regions in four models (two based on GN penalty and two based on TV penalty). Practically, we confirm the theoretical findings by measuring the accuracy of selected regions compared with ground truth on simulated data. We also evaluate the stability of recovered regions over cross-validation folds using real MRI data. Our findings show that the TV penalty is superior to the GN model. In addition, we showed that adding an L2 penalty improves the accuracy of estimated coefficients and selected significant regions for both types of models. (Joint work with Li Zhang, Dana Cobzas and Alan Wilman).
For Dr. Kong’s biosketch, please see https://sites.ualberta.ca/~lkong/index.html
Register in advance for this seminar via
https://phesc.zoom.us/meeting/register/tZYrc-qhqjsiHNbXv2D1FhdZyx7RVgFfQT4m