Performance improvement in computerized detection of cerebral aneurysms by retraining classifier using feedback data collected in routine reading environment

Abstract

Introduction: The performance of computer-assisted detection (CAD) software depends on the quality and quantity of the dataset used for supervised learning. To realize the continuous clinical use and performance improvement of CAD software, it is necessary to continuously collect data for supervised learning in practical use and to improve CAD software by retraining with the collected data. In this study, we investigated the performance improvement of cerebral aneurysm detection software based on retraining the classifier through a simulation-based study.

Methods: We collected data for retraining during the practical use of our cerebral aneurysm detection software and retrained the classifier for false positive (FP) reduction using the collected data. The effect on improving the performance was compared by changing the number of training cases and the training algorithms.

Results: The performance was improved significantly (p < .05) by retraining using additional training cases. In contrast, there were no statistical differences in the performance upon retraining among the four training algorithms for boosting. The sensitivity at 3 FPs/case was improved from 81.5% to 89.5% by retraining with additional training cases.

Conclusions: The performance of the software was effectively improved by adding training cases rather than by changing the training algorithm.