Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images

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👀 Introduction

Hepatocellular Carcinoma (HCC) relies on histopathological Whole Slide Images (WSIs) examination as the gold standard. However, manual analysis of these gigapixel, highly heterogeneous WSIs is labor-intensive and prone to inter-observer variability. This has catalyzed WSI-based Multi-modal Large Language Models (MLLMs) to enable VQA.

A key challenge in pathology MLLMs is gigapixel WSI representation. Existing methods either use thumbnail-based approaches that lose critical high-resolution diagnostic details, or employ slide-encoder approaches that generate excessively redundant tokens.

We propose Hepato-LLaVA, a specialized MLLM for fine-grained hepatocellular pathology analysis. It features a novel Hierarchical Sparse Visual Attention (HSVA) mechanism that models 2D tissue topology to aggregate diagnostic evidence while preserving context. To address multiscale data scarcity, we also present HepatoPathoVQA, comprising 33K hierarchically structured QA pairs validated by pathologists. Hepato-LLaVA achieves state-of-the-art diagnostic accuracy, outperforming existing pathology MLLMs by an absolute 20%.

Citation

@article{yang2026hepatollava,
  title={Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images},
  author={Yang, Yuxuan and Yan, Zhonghao and Zhang, Yi and Yun, Bo and Diao, Muxi and Zhao, Guowei and Liang, Kongming and Li, Wenbin and Ma, Zhanyu},
  journal={arXiv preprint arXiv:2602.19424},
  year={2026}
}