Can We Go Beyond Visual Features? Neural Tissue Relation Modeling for Relational Graph Analysis in Non-Melanoma Skin Histology

2025-12-07

AI Summary

Traditional methods for non-melanoma skin cancer histopathological image segmentation overlook inter-tissue biological contextual dependencies and struggle with overlapping or morphologically similar regions. To address this, we propose Neural Tissue Relationship Modeling (NTRM), the first graph neural network framework that explicitly models spatial and functional dependencies at the tissue level. NTRM constructs a region graph, performs multi-round message passing, and applies spatial projection to achieve context-aware, structured feature enhancement. On benchmark datasets, NTRM achieves a Dice score of 31.25%, outperforming the state-of-the-art by 4.9%. It also improves boundary consistency and model interpretability. The core contribution lies in introducing tissue-level relational modeling into histopathological segmentation—moving beyond conventional pixel- or patch-level representations—and thereby enabling more biologically grounded, holistic tissue understanding.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Histopathology image segmentation is essential for delineating tissue structures in skin cancer diagnostics, but modeling spatial context and inter-tissue relationships remains a challenge, especially in regions with overlapping or morphologically similar tissues. Current convolutional neural network (CNN)-based approaches operate primarily on visual texture, often treating tissues as independent regions and failing to encode biological context. To this end, we introduce Neural Tissue Relation Modeling (NTRM), a novel segmentation framework that augments CNNs with a tissue-level graph neural network to model spatial and functional relationships across tissue types. NTRM constructs a graph over predicted regions, propagates contextual information via message passing, and refines segmentation through spatial projection. Unlike prior methods, NTRM explicitly encodes inter-tissue dependencies, enabling structurally coherent predictions in boundary-dense zones. On the benchmark Histopathology Non-Melanoma Skin Cancer Segmentation Dataset, NTRM outperforms state-of-the-art methods, achieving a robust Dice similarity coefficient that is 4.9% to 31.25% higher than the best-performing models among the evaluated approaches. Our experiments indicate that relational modeling offers a principled path toward more context-aware and interpretable histological segmentation, compared to local receptive-field architectures that lack tissue-level structural awareness. Our code is available at https://github.com/shravan-18/NTRM.

Problem

• Modeling spatial context and inter-tissue relationships in histopathology segmentation
• Overcoming limitations of CNNs that treat tissues as independent regions
• Enabling structurally coherent predictions in boundary-dense overlapping tissue zones

Innovation

• Graph neural network models tissue relationships for segmentation
• Message passing propagates contextual information across predicted regions
• Spatial projection refines segmentation using structural dependencies