Quantitative digital microscopy with deep learning

Benjamin Midtvedt; Saga Helgadottir; Aykut Argun; Jesús Pineda; Daniel Midtvedt; Giovanni Volpe

doi:10.1117/12.2596979

1 August 2021 Quantitative digital microscopy with deep learning

Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180413 (2021) https://doi.org/10.1117/12.2596979
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

DeepTrack is an all-in-one deep learning framework for digital microscopy, attempting to bridge the gap between state of the art deep learning solutions and end-users. It provides tools for designing samples, simulating optical systems, training deep learning networks, and analyzing experimental data. We show the versatility of deep learning by solving a wide field of common problems in microscopy. Our hope is to serve as a platform for researchers to launch their solutions for the benifit of the entire field.

Conference Presentation

Citation Download Citation

Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, and Giovanni Volpe "Quantitative digital microscopy with deep learning", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180413 (1 August 2021); https://doi.org/10.1117/12.2596979

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