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\iteman{io-port 05978903}
\itemau{Zuluaga, Maria A.; Hush, Don; Delgado Leyton, Edgar J.F.; Hoyos, Marcela Hern\'andez; Orkisz, Maciej}
\itemti{Learning from only positive and unlabeled data to detect lesions in vascular CT images.}
\itemso{Fichtinger, Gabor (ed.) et al., Medical image computing and computer-assisted intervention -- MICCAI 2011. 14th international conference, Toronto, Canada, September 18--22, 2011. Proceedings, Part III. Berlin: Springer (ISBN 978-3-642-23625-9/pbk). Lecture Notes in Computer Science 6893, 9-16 (2011).}
\itemab
Summary: 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.
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\itemli{doi:10.1007/978-3-642-23626-6\_2}
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