Vision Transformer-based regression analysis to predict subnuclear chromatin organization from two-dimensional optical scattering signals
Year: 2026
Type: article
Abstract: Azimuth-resolved optical scattering signals from cell nuclei carry rich information about internal refractive index variations. These two-dimensional patterns provide valuable insights into chromatin organization which plays a critical role in early cancer detection. Our goal was to investigate whether two-dimensional scattering signals could serve as inputs to an inverse problem framework for extracting the spatial correlation length lc and fluctuation magnitude δn of subnuclear refractive index variations using state-of-the-art deep learning approaches. Deriving closed-form expressions connecting azimuth-resolved signals to lc and δn proves intractable, so we turned to a purely data-driven methodology. We implemented a Vision Transformer-based regression pipeline and evaluated it on 198 numerically simulated scattering patterns generated from nuclear models spanning a range of internal structural parameters. We observed strong agreement between ground truth and predicted values for both parameters, achieving mean absolute percent errors of 6.2 % for lc and 9.8 % for δn. Notably, these figures represent substantial improvements over prior Convolutional Neural Network-based methods and fall below the minimum relative spacing between adjacent parameter values in our dataset. Importantly, we demonstrate that the Vision Transformer achieves stable performance on this small dataset through careful regularization including weight decay (1·10–4), dropout (0.1), and early stopping (patience is 15), with learning curves showing no evidence of overfitting. These findings suggest that Vision Transformer architectures offer a promising avenue for mining the information encoded in two-dimensional optical scattering data, delivering markedly better accuracy than conventional Convolutional Neural Network approaches for quantitative chromatin characterization.
Authors: S. Azzaz-Rahmani, H. Zerrouki
Institution: Université Djilali de Sidi Bel Abbès
Language: Russian
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7204521556