Enumerated sparse extraction of important surgical planning features for mandibular reconstruction

Abstract

[2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2020); Montreal, Quebec, Canada, 20-24 July 2020]Because implicit medical knowledge and experience are used to perform medical treatment, such decisions must be clarified when systematizing surgical procedures. We propose an algorithm that extracts low-dimensional features that are important for determining the number of fibular segments in mandibular reconstruction using the enumeration of Lasso solutions (eLasso). To perform the multi-class classification, we extend the eLasso using an importance evaluation criterion that quantifies the contribution of the extracted features. Experiment results show that the extracted 7-dimensional feature set has the same estimation performance as the set using all 49-dimensional features