Asghar, Solomon
Ni, Ran https://orcid.org/0000-0001-9478-0674
Volpe, Giorgio https://orcid.org/0000-0001-9993-5348
Funding for this research was provided by:
National Research Foundation Singapore (Grant No. NRF- CRP29-2022-0002)
Chan Zuckerberg Initiative (2023-321188)
A*STAR-UCL Research Attachment Programme through the EPSRC M3S CDT (EP/L015862/1)
Singapore Ministry of Education (MOE2019-T2-2-010)
Article Title: U-Net 3+ for anomalous diffusion analysis enhanced with mixture estimates (U-AnD-ME) in particle-tracking data
Journal Title: Journal of Physics: Photonics
Article Type: paper
Copyright Information: © 2025 The Author(s). Published by IOP Publishing Ltd
Publication dates
Date Received: 2025-02-25
Date Accepted: 2025-08-08
Online publication date: 2025-08-21