{
  "abstract": "Fluorescence correlation spectroscopy (FCS) is widely used for quantifying molecular dynamics in live cells. However, conventional FCS relies on the autocorrelation function (ACF), a lossy transformation that discards higher-order information, mathematically couples distinct biophysical parameters, and requires long measurements (~50,000 frames, or ~1 minute) that obscure short-lived transient events. To overcome this information bottleneck, we introduce ImFCSViT, a spatiotemporal Vision Transformer-based framework that bypasses the ACF representation entirely, operating directly on raw fluorescence image stacks. By introducing a dedicated normalization token to preserve global intensity statistics, our approach simultaneously extracts four biophysical parameters (diffusion coefficient D, particle density N, particle brightness B, and PSF structure factor {sigma}0) in a single forward pass. Requiring only 2.5 s of data, and potentially down to 1 s (a 1-2 order reduction in data requirement), our approach accurately recovers diffusion coefficients while utilizing its spatial field-of-view to reveal spatially heterogeneous, slow-diffusing sub-populations in lipid bilayers and live-cell plasma membranes that are invisible to classical FCS. By leveraging the data-driven representational learning capabilities of deep learning, we present a flexible, generalizable, and powerful tool for mapping complex, multi-parameter spatiotemporal dynamics in biological systems.",
  "abstract_shown": true,
  "affiliations": [
    "National University of Singapore"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "S. R. Sim"
    },
    {
      "name": "Z. Wang"
    },
    {
      "name": "S. Zhou"
    },
    {
      "name": "S. Pandey"
    },
    {
      "affs": [
        0
      ],
      "name": "T. Wohland"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cbn81yqmktmp0.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.21.760315",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760315v1.full",
  "id": "cbn81yqmktmp0",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760315v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biophysics"
    ],
    [
      "openalex",
      "1304|Advanced Fluorescence Microscopy Techniques"
    ]
  ],
  "openalex": "W7223568351",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760315v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cbn81yqmktmp0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:32Z",
      "native_id": "10.64898/2026.10.21.760315",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760315v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "Vision Transformers as a Framework for Imaging Fluorescence Correlation Spectroscopy",
  "title_text": "Vision Transformers as a Framework for Imaging Fluorescence Correlation Spectroscopy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:32Z",
  "url": "https://research.draconitic.com/paper/cbn81yqmktmp0",
  "venue": "bioRxiv",
  "versions": []
}