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https://repositorio.uci.cu/jspui/handle/123456789/9484| Título : | Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
| Autor : | Balocco, Simone González, Mauricio Ñancule, Ricardo Radeva, Petia Thomas, Gabriel |
| Palabras clave : | INTRAVASCULAR ULTRASOUND IMAGES;CONVOLUTIONAL NETS;DEEP LEARNING;MEDICAL IMAGE ANALYSIS |
| Fecha de publicación : | 2018 |
| Editorial : | Springer |
| Citación : | 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 |
| Resumen : | 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 F1-measure, precision and recall. |
| URI : | https://repositorio.uci.cu/jspui/handle/123456789/9484 |
| Aparece en las colecciones: | UCIENCIA 2018 |
Ficheros en este ítem:
| Fichero | Tamaño | Formato | |
|---|---|---|---|
| A032.pdf | 102.11 kB | Adobe PDF | Visualizar/Abrir |
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