10 minute mailLack of classifier robustness to CT acquisition parameter variations is a barrier to widespread adoption of CAD systems. As part of his PhD research, CVIB graduate Dr Danny Chong developed a new Robustness-Driven Feature Selection (RDFS) algorithm that preferentially selects features that are relatively invariant CT technical factors.
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Working with CVIB faculty and staff, Dr. Chong showed that use of RDFS with 3D texture features improved CT classification of fibrotic interstitial lung disease in multi-center clinical trials. The research was published in the January 2016 issue of the IEEE Transactions on Medical Imaging.
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