The Anomalous Diffusion challenge: objective comparison of methods to decode anomalous diffusion

Gorka Muñoz-Gil; Giovanni Volpe; Miguel Angel García-March; Ralf Metzler; Maciej Lewenstein; Carlo Manzo

doi:10.1117/12.2595716

1 August 2021 The Anomalous Diffusion challenge: objective comparison of methods to decode anomalous diffusion

Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel García-March, Ralf Metzler, Maciej Lewenstein, Carlo Manzo

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180416 (2021) https://doi.org/10.1117/12.2595716
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

Deviations from Brownian motion leading to anomalous diffusion control transport mechanisms in many fields, from ecology to quantum physics. The detection of anomalous diffusion from an individual trajectory is a challenging task, which traditionally relies on calculating the mean square displacement. This approach finds its limits for cases of practical interest, e.g. short/noisy trajectories or ensembles of heterogeneous trajectories. Recently, new approaches have been proposed, mostly building on the ongoing machine-learning revolution. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition. Participants applied their own algorithms independently to a commonly defined data set including diverse scenarios. Although no single method performed best across all conditions, the results revealed clear differences between the various approaches, providing practical advice for users and a benchmark for developers.

Conference Presentation

Citation Download Citation

Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel García-March, Ralf Metzler, Maciej Lewenstein, and Carlo Manzo "The Anomalous Diffusion challenge: objective comparison of methods to decode anomalous diffusion", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180416 (1 August 2021); https://doi.org/10.1117/12.2595716

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