Subset selection for intracranial aneurysms for training simulations

Abstract

Abstract We present a framework with two options for selecting a subgroup of training cases for intracranial aneurysm (IA) treatment. Option one, general training, describes training cases that represent a variety of different IAs that represent the diversity of real world cases. This can be achieved via instance selection (IS), which reduces size of a dataset by eliminating redundancies via outlier removal and clustering. Option two, a specific training scenario, describes training that is specialized based on IA features of a specific case, for which we present the novel reverse instance selection (RIS), which introduces similarity to the specific case to the IS methodology. We evaluated our IS and RIS by comparing them to subsets selected based on similarity (SIM) and random sampling (RndS). RIS outperformed SIM and RndS in 79% of experiments. IS outperformed RndS in 33% of experiments. In both scenarios, we observed that our approaches, which balance the weaknesses of SIM and RndS, perform best for small subset sizes close to the database cluster size. Our IS and RIS are flexible in regards to the underlying machine learning and weighting of metrics for evaluation, thus providing a way to select a representative and diverse subset not just for IAs, but also for different kinds of data.

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