83 citations · 153 across the 6 of their papers we have counts for
6 papers
Learning minimal representations of stochastic processes with variational autoencoders
Gabriel Fernández-Fernández, Carlo Manzo, Maciej Lewenstein +2
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertaint…
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…
Inferring pointwise diffusion properties of single trajectories with deep learning
Borja Requena, Sergi Masó, Joan Bertran +3
In order to characterize the mechanisms governing the diffusion of particles in biological scenarios, it is essential to accurately determine their diffusive properties. To do so,…
Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion
Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi +4
The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Thanks to recent advan…
Objective comparison of methods to decode anomalous diffusion
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March +31
Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion fr…
Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering
Tina Kosǔta, Marta Cullell-Dalmau, Francesca Cella Zanacchi +1
Super-resolution imaging techniques have largely improved our capabilities to visualize nanometric structures in biological systems. Their application further enables one to potent…