Spatial Message Passing in Language Space for Pathology Image Interpretation
Multimodal Large Language Models (MLLMs) can generate pathological descriptions from histological images, but gigapixel Whole Slide Images (WSIs) exceed their visual context limits. The standard tiling workaround makes WSIs tractable yet severs the tissue neighborhoods that define tumor-stroma interfaces and morphology. We introduce Spatial Language Message Passing (SLMP), a framework that performs spatial reasoning entirely in language space, human-readable by construction. SLMP represents a WSI region as a spatial text graph: tiles are nodes initialized with MLLM descriptions, and edges enco
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “Spatial Message Passing in Language Space for Pathology Imag” ≈ “Tongyi-MAI/Z-Image-Turbo””
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Jing-Cheng Yang →
“Spatial Message Passing in Language Space for Pathology Image Interpretation”
- LinkedLinked via arxiv author · 85%Hao-Jung Wang →
“Spatial Message Passing in Language Space for Pathology Image Interpretation”
- LinkedLinked via arxiv author · 85%Jinhao Du →
“Spatial Message Passing in Language Space for Pathology Image Interpretation”
- LinkedLinked via arxiv author · 85%Ziyang Huang →
“Spatial Message Passing in Language Space for Pathology Image Interpretation”
- LinkedLinked via arxiv author · 85%Ming-shan Tsai →
“Spatial Message Passing in Language Space for Pathology Image Interpretation”