Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to enhance the in-vitro training set by including examples from in-vivo coronary plaques data set. For this purpose, a Sequential Floating Forward Selection method is applied on in-vivo data and plaque characterization performances are evaluated by Leave-One-Patient-Out cross-validation technique. Supervised data inclusion improves global classification accuracy from 89.39% to 91.82%. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Ciompi, F., Pujol, O., Rodriguez Leor, O., Gatta, C., Serrano Vida, A., & Radeva, P. (2009). Enhancing In-Vitro IVUS data for tissue characterization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5524 LNCS, pp. 241–248). https://doi.org/10.1007/978-3-642-02172-5_32
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