Deep learning approaches for imaging fluorescence correlation spectroscopy parameter estimation with limited data sets

Abstract

Deep Learning approaches using Convolutional Neural Networks (CNNs) are successfully applied in many disciplines and especially in imaging applications. CNNs provide advantages in terms of data processing time, required data size to reach a particular precision in parameter estimation, as well as the possibility of unsupervised data evaluation. Here we propose two different approaches to estimate diffusion coefficients from Imaging Fluorescence Correlation Spectroscopy (Imaging FCS) experiments and compare their performance with standard non-linear least squares (NLS) fits.