most citedPhysics-informed neural networks for diffraction tomography

9 citations · 9 across the 2 of their papers we have counts for

collaborators

2 papers

physics.optics2022★ 9 cited

Physics-informed neural networks for diffraction tomography

Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub +1

We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmh…

physics.optics2022

Single-cell phase-contrast tomograms data encoded by 3D Zernike descriptors

Pasquale Memmolo, Daniele Pirone, Daniele G. Sirico +5

Phase-contrast tomographic flow cytometry combines quantitative 3D analysis of unstained single cells and high-throughput. A crucial issue of this method is the storage and managem…