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

arXiv: https://arxiv.org/abs/2512.06949 · HTML: https://mlsanity.com/paper/2512.06949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 56 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention](https://mlsanity.com/paper/2404.07143/) (2024)
- [How Transformers Learn Causal Structure with Gradient Descent](https://mlsanity.com/paper/2402.14735/) (2024)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)
- [Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2403.07719/) (2024)
- [Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision](https://mlsanity.com/paper/2303.00885/) (2023)
- [Selective Attention Improves Transformer](https://mlsanity.com/paper/2410.02703/) (2024)
- [Selective Attention: Enhancing Transformer through Principled Context Control](https://mlsanity.com/paper/2411.12892/) (2024)
- [Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model](https://mlsanity.com/paper/2304.03572/) (2023)
- [Multi-Scale Prototypical Transformer for Whole Slide Image Classification](https://mlsanity.com/paper/2307.02308/) (2023)
- [Cell Graph Transformer for Nuclei Classification](https://mlsanity.com/paper/2402.12946/) (2024)
- [TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification](https://mlsanity.com/paper/2303.05780/) (2023)
- [Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images](https://mlsanity.com/paper/2212.08781/) (2022)
- [Low-Cost FlashAttention with Fused Exponential and Multiplication Hardware Operators](https://mlsanity.com/paper/2505.14314/) (2025)
- [DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing Attention](https://mlsanity.com/paper/2410.08582/) (2024)
- [ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathology](https://mlsanity.com/paper/2510.20754/) (2025)
- [Unsupervised Latent Stain Adaptation for Computational Pathology](https://mlsanity.com/paper/2406.19081/) (2024)
- [PathSegDiff: Pathology Segmentation using Diffusion model representations](https://mlsanity.com/paper/2504.06950/) (2025)
- [SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification](https://mlsanity.com/paper/2407.08968/) (2024)
- [Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2410.10260/) (2024)
- [Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2508.05382/) (2025)
- [A Graph-Based Framework for Interpretable Whole Slide Image Analysis](https://mlsanity.com/paper/2503.11846/) (2025)
- [SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome](https://mlsanity.com/paper/2511.15464/) (2025)

## Abstract (arXiv)

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.