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Communication Dans Un Congrès Année : 2023

Multivariate curve resolution with autoencoders for CARS microspectroscopy

Résumé

Coherent anti-Stokes Raman scattering (CARS) microspectroscopy is a powerful tool for label-free cell imaging thanks to its ability to acquire a rich amount of information. An important family of operations applied to such data is multivariate curve resolution (MCR). It aims to find main components of a dataset and compute their spectra and concentrations in each pixel. Recently, autoencoders began to be studied to accomplish MCR with dense and convolutional models. However, many questions, like the results variability or the reconstruction metric, remain open and applications are limited to hyperspectral imaging. In this article, we present a nonlinear convolutional encoder combined with a linear decoder to apply MCR to CARS microspectroscopy. We conclude with a study of the result variability induced by the encoder initialization.
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Dates et versions

hal-03831753 , version 1 (04-04-2023)

Identifiants

  • HAL Id : hal-03831753 , version 1

Citer

Damien Boildieu, David Helbert, Amandine Magnaudeix, Philippe Leproux, Philippe Carré. Multivariate curve resolution with autoencoders for CARS microspectroscopy. Computational Imaging Conference, IS&T Electronic Imaging, Jan 2023, San Francisco, United States. ⟨hal-03831753⟩
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