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|Author||Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell|
|Title||Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer||Type||Conference Article|
|Year||2017||Publication||6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging||Abbreviated Journal|
|Abstract||Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
|Address||Quebec; Canada; September 2017|
|Publisher||Place of Publication||Editor|
|Language||Summary Language||Original Title|
|Series Editor||Series Title||Abbreviated Series Title||LNCS|
|Series Volume||Series Issue||Edition|
|Notes||IAM; 600.096; 600.075; 600.145||Approved||no|
|Call Number||Admin @ si @ GRM2017||Serial||2957|
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