Quantitative digital microscopy with deep learning

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

doi:10.1117/12.2567479

20 August 2020 Quantitative digital microscopy with deep learning

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

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691B (2020) https://doi.org/10.1117/12.2567479
Event: SPIE Nanoscience + Engineering, 2020, Online Only

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. Moreover, the framework is packaged with an easy-to-use graphical user interface, designed to solve standard microscopy problems with no required programming experience. By specifically designing the framework with modularity and extendability in mind, we allow new methods to easily be implemented and combined with previous applications.

Conference Presentation

Citation Download Citation

Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Daniel Midtvedt, and Giovanni Volpe "Quantitative digital microscopy with deep learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691B (20 August 2020); https://doi.org/10.1117/12.2567479

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