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Proceedings Paper

How to choose and optimize a classifier for your polarimetric imaging data
Author(s): Jean Rehbinder; Christian Heinrich; Angelo Pierangelo; Jihad Zallat
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Paper Abstract

Mueller polarimetry is a powerful characterization technique for a variety of samples and a promising optical-biopsy tool for early detection of cancer. Recent advances in Mueller imaging devices allow the collection of large ex-vivo and invivo image databases. Although the technique is sensitive to subtle changes in the micro-organization of tissue, the Mueller matrices of such complex media contain intertwined polarimetric effects and are difficult to interpret. To identify the polarimetric signature of a given tissue modification (cancerous or not), machine learning tools are particularly well suited. However, a statistically sound approach is needed to make the most out of these tools and avoid common pitfalls. We present a global statistical framework based on decision theory. It consists of a complete preprocessing and analysis pipeline for polarimetric bioimages. In the analysis stage, we use a loss-risk-based approach to automatically select the optimal classifier among a library of classifiers. The approach allows to determine the subset of polarimetric parameters of interest, to determine the parameters of the classifiers and to assess classifier performance using cross-validation. The proposed framework is illustrated with precancer detection on human ex-vivo cervical samples.

Paper Details

Date Published: 20 February 2020
PDF: 7 pages
Proc. SPIE 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020, 1125110 (20 February 2020); doi: 10.1117/12.2546032
Show Author Affiliations
Jean Rehbinder, Univ. de Strasbourg (France)
Christian Heinrich, Univ. de Strasbourg (France)
Angelo Pierangelo, Lab. de Physique des Interfaces et des Couches Minces, CNRS (France)
Ecole Polytechnique (France)
Univ. Paris Saclay (France)
Jihad Zallat, Univ. de Strasbourg (France)


Published in SPIE Proceedings Vol. 11251:
Label-free Biomedical Imaging and Sensing (LBIS) 2020
Natan T. Shaked; Oliver Hayden, Editor(s)

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