AnomalousNet: a hybrid approach with attention U-Nets and change point detection for accurate characterization of anomalous diffusion in video data
[EN] Anomalous diffusion is ubiquitous in systems ranging from intracellular transport and porous-medium flow to animal foraging, and its quantification requires robust methods that cope with short and noisy trajectories. We present AnomalousNet, a unified three-stage pipeline for analyzing anomalou...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:riunet.upv.es:10251/227577 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/227577 |
| Access Level: | Acceso aberto |
| Palavra-chave: | U-Net Anomalous diffusion Fractional Brownian motion Change point detection AnDi Challenge |
| Resumo: | [EN] Anomalous diffusion is ubiquitous in systems ranging from intracellular transport and porous-medium flow to animal foraging, and its quantification requires robust methods that cope with short and noisy trajectories. We present AnomalousNet, a unified three-stage pipeline for analyzing anomalous particle dynamics. First, up to 64 particles per 128x128 field of view are tracked using the Crocker-Grier algorithm via Trackpy. Next, an Attention U-Net trained on over 8.6 x 104 simulated experiments infers frame-wise anomalous exponents alpha, generalized diffusion coefficients K, and discrete motion states. Finally, regime changes are identified using an L2-regularized, windowed pruned exact linear time change-point detection algorithm. On the 2nd Anomalous Diffusion Challenge benchmark the method achieved MAE(alpha)=0.32, MSLE(K)=0.10, state-classification F1=0.93, and change-point RMSE = 0.09, ranking second in the video single-trajectory task and ensemble task. These results demonstrate precise discrimination of subdiffusive, normal, and superdiffusive regimes, and frame-level identification of state transitions establishing AnomalousNet as a powerful tool for quantitative analysis of heterogeneous diffusion in video data. |
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