Learning from only positive and unlabeled data to detect lesions in vascular CT images

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Abstract

Detecting vascular lesions is an important task in the diagnosis and follow-up of the coronary heart disease. While most existing solutions tackle calcified and non-calcified plaques separately, we present a new algorithm capable of detecting both types of lesions in CT images. It builds up on a semi-supervised classification framework, in which the training set is made of both unlabeled data and a small amount of data labeled as normal. Our method takes advantage of the arrival of newly acquired data to re-train the classifier and improve its performance. We present results on synthetic data and on datasets from 15 patients. With a small amount of labeled training data our method achieved a 89.8% true positive rate, which is comparable to state-of-the-art supervised methods, and the performance can improve after additional iterations. © 2011 Springer-Verlag.

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Zuluaga, M. A., Hush, D., Delgado Leyton, E. J. F., Hoyos, M. H., & Orkisz, M. (2011). Learning from only positive and unlabeled data to detect lesions in vascular CT images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6893 LNCS, pp. 9–16). Springer Verlag. https://doi.org/10.1007/978-3-642-23626-6_2

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