The anomalous diffusion challenge: single trajectory characterisation as a competition

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

doi:10.1117/12.2567914

20 August 2020 The anomalous diffusion challenge: single trajectory characterisation as a competition

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

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691C (2020) https://doi.org/10.1117/12.2567914
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

The deviation from pure Brownian motion, generally referred to as anomalous diffusion, has received large attention in the scientific literature to describe many physical scenarios. Several methods, based on classical statistics and machine learning approaches, have been developed to characterize anomalous diffusion from experimental data, which are usually acquired as particle trajectories. With the aim to assess and compare the available methods to characterize anomalous diffusion, we have organized the Anomalous Diffusion (AnDi) Challenge (http://www.andi-challenge.org/). Specifically, the AnDi Challenge will address three different aspects of anomalous diffusion characterization, namely: (i) Inference of the anomalous diffusion exponent. (ii) Identification of the underlying diffusion model. (iii) Segmentation of trajectories. Each problem includes sub-tasks for different number of dimensions (1D, 2D and 3D). In order to compare the various methods, we have developed a dedicated open-source framework for the simulation of the anomalous diffusion trajectories that are used for the training and test datasets. The challenge was launched on March 1, 2020, and consists of three phases. Currently, the participation to the first phase is open. Submissions will be automatically evaluated and the performance of the top-scoring methods will be thoroughly analyzed and compared in an upcoming article.

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: single trajectory characterisation as a competition", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691C (20 August 2020); https://doi.org/10.1117/12.2567914

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available