Revealing the spatiotemporal fingerprint of microscopic motion using geometric deep learning (Conference Presentation)

Jesús D. Pineda Castro; Benjamin Midtvedt; Harshith Bachimanchi; Sergio Nóe; Daniel Midtvedt; Giovanni Volpe; Carlo Manzo

doi:10.1117/12.2633593

4 October 2022 Revealing the spatiotemporal fingerprint of microscopic motion using geometric deep learning (Conference Presentation)

Jesús D. Pineda Castro, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Nóe, Daniel Midtvedt, Giovanni Volpe, Carlo Manzo

Author Affiliations +

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040G (2022) https://doi.org/10.1117/12.2633593
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

This work introduces MAGIK, a geometric deep learning framework for characterizing dynamic properties from time-lapse microscopy. MAGIK exploits geometric deep learning capability to capture the full spatiotemporal complexity of biological experiments using Graph Attention Networks. By processing object features with geometric priors, the neural network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties of the biological system. We demonstrate the flexibility and reliability of MAGIK by applying it to real and simulated data corresponding to a broad range of biological experiments.

Conference Presentation

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Jesús D. Pineda Castro, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Nóe, Daniel Midtvedt, Giovanni Volpe, and Carlo Manzo "Revealing the spatiotemporal fingerprint of microscopic motion using geometric deep learning (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040G (4 October 2022); https://doi.org/10.1117/12.2633593

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KEYWORDS

Biological research

Image acquisition

Microscopy

Molecules

Neural networks

Reliability

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