Abstract
The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of \(F_1\)-measure, precision and recall.
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Notes
- 1.
Note that an average \(F_1\) score computed among 10 trials is not necessarily equal to \(2(\bar{P}\bar{R})/(\bar{P}+\bar{R})\) where \(\bar{P}\) is the average precision and \(\bar{R}\) the average recall.
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Balocco, S., González, M., Ñanculef, R., Radeva, P., Thomas, G. (2018). Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_4
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DOI: https://doi.org/10.1007/978-3-030-01132-1_4
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