Skip to main content
archive
Search Submit Donate Log in
Press Enter to search · Advanced search

Computer Science > Computer Vision and Pattern Recognition

arXiv:2603.23067 (cs)
[Submitted on 24 Mar 2026 (v1), last revised 25 Mar 2026 (this version, v2)]

Title:MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding

Authors:Basit Alawode, Arif Mahmood, Muaz Khalifa Al-Radi, Shahad Albastaki, Asim Khan, Muhammad Bilal, Moshira Ali Abdalla, Mohammed Bennamoun, Sajid Javed
View a PDF of the paper titled MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding, by Basit Alawode and 8 other authors
View PDF HTML (experimental)
Abstract:Whole Slide Images (WSIs) exhibit hierarchical structure, where diagnostic information emerges from cellular morphology, regional tissue organization, and global context. Existing Computational Pathology (CPath) Multimodal Large Language Models (MLLMs) typically compress an entire WSI into a single embedding, which hinders fine-grained grounding and ignores how pathologists synthesize evidence across different scales. We introduce \textbf{MLLM-HWSI}, a Hierarchical WSI-level MLLM that aligns visual features with pathology language at four distinct scales, cell as word, patch as phrase, region as sentence, and WSI as paragraph to support interpretable evidence-grounded reasoning. MLLM-HWSI decomposes each WSI into multi-scale embeddings with scale-specific projectors and jointly enforces (i) a hierarchical contrastive objective and (ii) a cross-scale consistency loss, preserving semantic coherence from cells to the WSI. We compute diagnostically relevant patches and aggregate segmented cell embeddings into a compact cellular token per-patch using a lightweight \textit{Cell-Cell Attention Fusion (CCAF)} transformer. The projected multi-scale tokens are fused with text tokens and fed to an instruction-tuned LLM for open-ended reasoning, VQA, report, and caption generation tasks. Trained in three stages, MLLM-HWSI achieves new SOTA results on 13 WSI-level benchmarks across six CPath tasks. By aligning language with multi-scale visual evidence, MLLM-HWSI provides accurate, interpretable outputs that mirror diagnostic workflows and advance holistic WSI understanding. Code is available at: \href{this https URL}{GitHub}.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2603.23067 [cs.CV]
  (or arXiv:2603.23067v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2603.23067
arXiv-issued DOI via DataCite

Submission history

From: Basit Alawode [view email]
[v1] Tue, 24 Mar 2026 11:04:58 UTC (9,863 KB)
[v2] Wed, 25 Mar 2026 07:41:44 UTC (12,211 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding, by Basit Alawode and 8 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source
license icon view license

Current browse context:

cs.CV
< prev   |   next >
new | recent | 2026-03
Change to browse by:
cs

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
Loading...

BibTeX formatted citation

Data provided by:

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
Simons Foundation Simons Foundation International Schmidt Sciences