challenge 2020

The results of the AnDi challenge 2020 are in the article: Muñoz-Gil et al. Objective comparison of methods to decode anomalous diffusion, Nature Communications 12:6253 (2021).

Objective and tasks

The competition was aimed to assess the performance of methods in quantifying anomalous diffusion, with respect to three different tasks:

• T1 – anomalous exponent inference
• T2 – model classification
• T3 – trajectory segmentation

each for 1D, 2D, and 3D trajectories. The performance was assessed with common metrics on simulated datasets with trajectory length and signal-to-noise level reproducing realistic experimental conditions. The submitted methods were also compared on the blind analysis of experimental trajectories.

Participants

team/software	method	task1	task2	task3	platform	open access	contact
Anomalous Unicorns HYDRAS	Ensemble of CNN and RNN	1D	1D	-	Python	GitHub	Borja Requena ICFO-The Institute of Photonic Sciences Castelldefels (Barcelona), Spain
BIT	Bayesian inference	1D+2D+3D	1D+2D+3D	1D+2D+3D	Matlab	GitHub	Michael A. Lomholt PhyLife, Department of Physics, Chemistry and Pharmacy, University of Southern Denmark Odense M, Denmark
DecBayComp Gratin	Graph neural network	1D	1D	-	Python	GitHub	Jean-Baptiste Masson Institut Pasteur, Decision and Bayesian Computation lab Paris, France
DeepSPT	ResNet + XGBoost	1D	1D		Python	GitHub	Taegeun Song Center for AI and Natural Sciences, Korea Institute for Advanced Study Seoul, Korea
eduN RANDI	RNN + Dense NN	1D+2D+3D	1D+2D+3D	1D+2D+3D	Python	GitHub	Stefano Bo Max Planck Institute for the Physics of Complex Systems (MPI-PKS) Dresden, Germany
Erasmus MC FEST	bi-LSTM + Dense NN	1D+2D+3D	1D+2D+3D		Python	GitHub	Hélène Kabbech Erasmus MC, Department of Cell Biology Rotterdam, The Netherlands
FCI	CNN	1D+2D	1D+2D	1D+2D	Python	GitHub	Tom Bland The Francis Crick Institute London, UK
HNU Just LSTM it	LSTM	1D+2D+3D			Python	GitHub	Zihan Huang School of Physics and Electronics, Hunan University Changsha, China
NOA	CNN + bi-LSTM	1D			Python	GitHub	Jose Alberto Conejero Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València Valencia, Spain
QuBI AnDi-ELM	ELM	1D	1D		Matlab	GitHub	Carlo Manzo Facultat de Ciències i Tecnologia, UVIC-UCC Vic, Spain
TSA	Scaling analysis and feature engineering	1D+2D+3D	1D		Python	GitHub	Erez Aghion Max Planck Institute for the Physics of Complex Systems (MPI-PKS) Dresden, Germany
UCL CONDOR	Feature engineering + NN	1D+2D+3D	1D+2D+3D		Matlab	GitHub	Giorgio Volpe Department of Chemistry, University College London London, UK
UPV-MAT	CNN + bi-LSTM	1D+2D+3D	1D+2D+3D		Python	GitHub	Òscar Garibo i Orts Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València Valencia, Spain
Wust ML A	1D: RISE + forest classier 2D: MrSEQL + logistic reg.		1D+2D+3D		Python	GitHub	Janusz Szwabiński Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology Wrocław, Poland
Wust ML B	Gradient boosting regression + classifier	1D+2D	1D+2D+3D		Python	T1: GitHub T2: GitHub	Hanna Loch-Olszewska & Patrycja Kowalek Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology Wrocław, Poland

Datasets

For the final phase of the competitions, methods were evaluated over the test dataset 2020. Datasets were simulated using the andi-datasets package.

Organizers

• Gorka Muñoz-Gil & Maciej Lewenstein, Quantum Optics Theory – ICFO
• Carlo Manzo, the QuBI lab – FCT, UVic-UCC
• Giovanni Volpe, Soft Matter Lab – University of Gothenburg
• Miguel A. Garcia-March, UPV
• Ralf Metzler, Theoretical Physics – UniPotsdam