---
title: "VINTER: a generative vision-language model for spatial tissue annotation and interrogation"
id: "biorxiv-7-vinter-a-generative-vision-language-model-for-annotating-and-interrogating"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-7-vinter-a-generative-vision-language-model-for-annotating-and-interrogating"
content_type: "clinical_feed_article"
specialty: "Radiology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1?rss=1"
published_at: "2026-10-08T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# VINTER: a generative vision-language model for spatial tissue annotation and interrogation
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-7-vinter-a-generative-vision-language-model-for-annotating-and-interrogating
- **Specialty:** [Radiology](https://medichelpline.com/clinical-feed/radiology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1?rss=1)
- **Published At:** 2026-10-08T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- **VINTER** is a generative vision-language model aligned on 1.45 million histology-caption pairs to read histology, measured gene expression and spatial neighbourhood together as a single sequence. - The model returns a probability distribution over tissue classes rather than a single hard label, enabling interrogation of classification uncertainty and mixed tissue states. - Because measured genes are ingested as text tokens, the model’s molecular evidence can be edited with the image held constant, allowing in silico perturbations of molecular input. - In benchmarked tasks, VINTER annotated tissue more accurately than existing spatial-domain methods and a spatial foundation model and generalized across cohorts without requiring target labels for retraining. - Editing only the spatial neighbourhood input revealed the follicular B-cell and plasma-cell programmes that underlie VINTER’s interpretation of tertiary lymphoid structure (TLS) maturation in lung and kidney cancer. - VINTER’s probabilistic outputs exposed hidden heterogeneity in triple-negative breast cancer, including immune diversity within stroma and cancerous signals masked by benign labels. - Without retraining, VINTER transferred to higher-resolution and single-cell spatial platforms (Visium HD and Xenium) and resolved follicular architecture in colorectal cancer TLS. - The model reframes spatial annotation as an interrogable, multi-source reading of tissue structure and molecular state across platforms and resolutions. - Supplementary methodological specifics, dataset composition beyond the 1.45M caption pairs, and full implementation details were not reported in the source abstract.
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Jinyu Wu 1 The University of Tokyo; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jinyu%2BWu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wu%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJinyu%2BWu%2B) Wanzhe Xu 1 The University of Tokyo; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Wanzhe%2BXu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Xu%20W&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AWanzhe%2BXu%2B) * [ORCID record for Wanzhe Xu](http://orcid.org/0009-0003-2819-7482 "Open in new tab") Yizhou Zhuang 1 The University of Tokyo; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yizhou%2BZhuang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Zhuang%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYizhou%2BZhuang%2B) * [ORCID record for Yizhou Zhuang](http://orcid.org/0009-0001-9385-8887 "Open in new tab") Yawen Zhang 1 The University of Tokyo; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yawen%2BZhang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Zhang%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYawen%2BZhang%2B) Martin de Jesus Loza Lopez 1 The University of Tokyo; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Martin%2Bde%2BJesus%2BLoza%2BLopez%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Loza%20Lopez%20MD&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AMartin%2Bde%2BJesus%2BLoza%2BLopez%2B) * [ORCID record for Martin de Jesus Loza Lopez](http://orcid.org/0000-0002-3342-2643 "Open in new tab") yitao yang 1 The University of Tokyo; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=yitao%2Byang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=yang%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3Ayitao%2Byang%2B) Kenta Nakai 2 Institute of Medical Science, University of Tokyo * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Kenta%2BNakai%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Nakai%20K&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AKenta%2BNakai%2B) * [ORCID record for Kenta Nakai](http://orcid.org/0000-0002-8721-8883 "Open in new tab") * For correspondence: knakai@ims.u-tokyo.ac.jp * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5849960/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5849960/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5849960/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5849960/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5849960/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Spatial transcriptomics links gene expression to tissue structure, yet current models learn embeddings whose meaning is assigned afterwards or read histology without measured expression. Tissue decisions are therefore rarely made from morphology, genes and neighbourhood together, or examined through them. Here we introduce VINTER, a generative vision-language model aligned on 1.45 million histology-caption pairs that reads histology, measured gene expression and spatial neighbourhood as one sequence. It returns the probability of every tissue class, and because genes enter as text, its molecular evidence can be edited with the image held fixed. Reading all three sources together, VINTER annotated tissue more accurately than spatial-domain methods and a spatial foundation model, and transferred across cohorts without target labels. Editing only the neighbourhood resolved the follicular B-cell and plasma-cell programmes behind its reading of tertiary lymphoid structure (TLS) maturation in lung and kidney cancer. Its class probabilities exposed what single labels conceal in triple-negative breast cancer: immune heterogeneity within stroma and cancer hidden beneath benign labels. Without retraining, the same model transferred to Visium HD and single-cell Xenium data and resolved follicular architecture within colorectal cancer TLS. VINTER thus turns spatial annotation into an interrogable reading of tissue across platforms and resolutions. ### Competing Interest Statement The authors have declared no competing interest. Copyright [ The copyright holder has placed this preprint in the Public Domain.](https://creativecommons.org/publicdomain/zero/1.0/) It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/07/2026.10.01.752251.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1.supplementary-material) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) VINTER: a generative vision-language model for annotating and interrogating spatial tissues Jinyu Wu, Wanzhe Xu, Yizhou Zhuang, Yawen Zhang, Martin de Jesus Loza Lopez, yitao yang, Kenta Nakai bioRxiv 2026.10.01.752251; doi: https://doi.org/10.64898/2026.10.01.752251 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. Share This Article: Copy [![Twitter logo](https://www.biorxiv.org/sites/all/modules/highwire/highwire/images/twitter.png)](https://www.biorxiv.org/highwire_log/share/twitter?link=http%3A%2F%2Ftwitter.com%2Fshare%3Furl%3Dhttps%253A%2F%2Fwww.biorxiv.org%2Fcontent%2F10.64898%2F2026.10.01.752251v1%26text%3DVINTER%253A%2520a%2520generative%2520vision-language%2520model%2520for%2520annotating%2520and%2520interrogating%2520spatial%2520tissues "Share this on Twitter") [![Facebook logo](https://www.biorxiv.org/sites/all/modules/highwire/highwire/images/fb-blue.png)](https://www.biorxiv.org/highwire_log/share/facebook?link=http%3A%2F%2Fwww.facebook.com%2Fsharer.php%3Fu%3Dhttps%253A%2F%2Fwww.biorxiv.org%2Fcontent%2F10.64898%2F2026.10.01.752251v1%26t%3DVINTER%253A%2520a%2520generative%2520vision-language%2520model%2520for%2520annotating%2520and%2520interrogating%2520spatial%2520tissues "Share on Facebook") [![LinkedIn logo](https://www.biorxiv.org/sites/all/modules/highwire/highwire/images/linkedin-32px.png)](https://www.biorxiv.org/highwire_log/share/linkedin?link=http%3A%2F%2Fwww.linkedin.com%2FshareArticle%3Fmini%3Dtrue%26url%3Dhttps%253A%2F%2Fwww.biorxiv.org%2Fcontent%2F10.64898%2F2026.10.01.752251v1%26title%3DVINTER%253A%2520a%2520generative%2520vision-language%2520model%2520for%2520annotating%2520and%2520interrogating%2520spatial%2520tissues%26summary%3D%26source%3DbioRxiv "Publish this post to LinkedIn") [![Mendeley logo](https://www.biorxiv.org/sites/all/modules/highwire/highwire/images/mendeley.png)](https://www.biorxiv.org/highwire_log/share/mendeley?link=http%3A%2F%2Fwww.mendeley.com%2Fimport%2F%3Furl%3Dhttps%253A%2F%2Fwww.biorxiv.org%2Fcontent%2F10.64898%2F2026.10.01.752251v1%26title%3DVINTER%253A%2520a%2520generative%2520vision-language%2520model%2520for%2520annotating%2520and%2520interrogating%2520spatial%2520tissues "Share on Mendeley") [ Citation Tools](https://www.biorxiv.org/ "Citation Tools") [ Get QR code](https://connect.biorxiv.org/qr/2026.10.01.752251) * [Tweet Widget](http://twitter.com/share?url=https%3A//www.biorxiv.org/content/10.64898/2026.10.01.752251v1&count=horizontal&via=&text=VINTER%3A%20a%20generative%20vision-language%20model%20for%20annotating%20and%20interrogating%20spatial%20tissues&counturl=www.biorxiv.org/content/10.64898/2026.10.01
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