Machine Learning Aided Nonlinear Polarimetric Microscopy with Texture Analysis for Melanoma Tissue Characterization

Martynas Riauka; Viktoras Mazeika; Mykolas Maciulis; Edvardas Zurauskas; Lukas Kontenis; Lukas Kontenis; Kamdin Mirsanaye; Kamdin Mirsanaye; Virginijus Barzda; Virginijus Barzda; Virginijus Barzda

doi:10.1364/NLO.2021.NTh1A.5

OSA Nonlinear Optics 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper NTh1A.5
•https://doi.org/10.1364/NLO.2021.NTh1A.5

Machine Learning Aided Nonlinear Polarimetric Microscopy with Texture Analysis for Melanoma Tissue Characterization

Martynas Riauka, Viktoras Mazeika, Mykolas Maciulis, Edvardas Zurauskas, Lukas Kontenis, Kamdin Mirsanaye, and Virginijus Barzda

Nonlinear Optics 2021

Washington, DC United States
9–13 August 2021
ISBN: 978-1-943580-97-2

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Topological, Non-Hermitian, and Neuromorphic Systems (NTh1A)

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M. Riauka, V. Mazeika, M. Maciulis, E. Zurauskas, L. Kontenis, K. Mirsanaye, and V. Barzda, "Machine Learning Aided Nonlinear Polarimetric Microscopy with Texture Analysis for Melanoma Tissue Characterization," in OSA Nonlinear Optics 2021, R. Boyd, C. Conti, D. Christodoulides, and P. Rakich, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper NTh1A.5.

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Abstract

Machine learning aided polarimetric nonlinear microscopy and texture analysis of collagen in cancerous and normal skin tissues revealed the differences in polarimetric and texture parameter distributions suggesting an altered collagen structure.

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