We propose an approach for providing well-calibrated confidence measures for determining cerebrovascular risk stratification based on characteristics from noninvasive ultrasound imaging of carotid plaques. An important challenge we address is the class imbalance problem inherent in the particular task. The proposed approach is based on a novel framework, called conformal prediction (CP), for developing techniques that output sets of predictions guaranteed to contain the true classification of a new case with a prespecified probability. We follow a modified version of the CP framework, called Label-conditional Mondrian conformal prediction (LCMCP), so that the guarantee provided by CP does not only hold for all instances together, but also hold for the instances of each class independently, thus making prediction sets unbiased. Furthermore, LCMCP is combined with an underbagging ensemble of artificial neural networks so that its outputs are based on unbiased estimates. The important positive properties of the proposed approach are demonstrated experimentally on a dataset of patients that were followed up for eight years and had asymptomatic internal carotid artery stenosis at the baseline.
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Papadopoulos, H., Kyriacou, E. & Nicolaides, A. Unbiased confidence measures for stroke risk estimation based on ultrasound carotid image analysis. Neural Comput & Applic 28, 1209–1223 (2017). https://doi.org/10.1007/s00521-016-2590-3
- Conformal prediction
- Class imbalance
- Confidence measures
- Stroke risk assessment
- Plaque imaging
- Computer-aided diagnosis
- Ultrasound image analysis