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

• URL: http://arxiv.org/abs/2512.06949v1
• Date: Sun, 07 Dec 2025 18:04:29 GMT
• Title: Can We Go Beyond Visual Features? Neural Tissue Relation Modeling for Relational Graph Analysis in Non-Melanoma Skin Histology
• Authors: Shravan Venkatraman, Muthu Subash Kavitha, Joe Dhanith P R, V Manikandarajan, Jia Wu,
• Abstract summary: Histopathology image segmentation is essential for delineating tissue structures in skin cancer diagnostics.<n>Current convolutional neural network (CNN)-based approaches operate primarily on visual texture.<n>We introduce Tissue Relation Modeling (NTRM), a novel framework that augments CNNs with a tissue-level graph neural network to model spatial and functional relationships across tissue types.
• Score: 7.199970129045625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Spatiotemporal Graph Learning with Direct Volumetric Information Passing and Feature Enhancement [62.91536661584656]
  We propose a dual-module framework, Cell-embedded and Feature-enhanced Graph Neural Network (aka, CeFeGNN) for learning.<n>We embed learnable cell attributions to the common node-edge message passing process, which better captures the spatial dependency of regional features.<n>Experiments on various PDE systems and one real-world dataset demonstrate that CeFeGNN achieves superior performance compared with other baselines.
  arXiv  Detail & Related papers  (2024-09-26T16:22:08Z)
• Synthetic Data for Robust Stroke Segmentation [0.0]
  Current deep learning-based approaches to lesion segmentation in neuroimaging often depend on high-resolution images and extensive annotated data.<n>This paper introduces a novel synthetic data framework tailored for stroke lesion segmentation.<n>Our approach trains models with label maps from healthy and stroke datasets, facilitating segmentation across both normal and pathological tissue.
  arXiv  Detail & Related papers  (2024-04-02T13:42:29Z)
• Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification [0.0]
  We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements.
  arXiv  Detail & Related papers  (2023-07-24T13:39:21Z)
• Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis [1.943314771739382]
  Graph neural networks (GNNs) offer a promising solution by coding the spatial relationships within images. We present a novel approach using a heterogeneous GNN that captures the spatial and hierarchical relations between cell and tissue graphs. We also compare the performance of a cross-attention-based network and a transformer architecture for modeling the intricate relationships within tissue and cell graphs.
  arXiv  Detail & Related papers  (2023-07-16T19:06:29Z)
• TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance [66.43360974979386]
  We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language.
  arXiv  Detail & Related papers  (2023-07-08T14:10:37Z)
• SpatioTemporal Focus for Skeleton-based Action Recognition [66.8571926307011]
  Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information.
  arXiv  Detail & Related papers  (2022-03-31T02:45:24Z)
• Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input [1.5328185694137677]
  We propose an efficient interactive segmentation network that requires minimum input from the user to accurately annotate different tissue types in the histology image. We show that not only does our proposed method speed up the interactive annotation process, it can also outperform the existing automatic and interactive region segmentation models.
  arXiv  Detail & Related papers  (2021-08-30T16:43:03Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification [11.051615198681565]
  We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue. It consists of a low-level cell-graph, capturing cell morphology and interactions, a high-level tissue-graph, capturing morphology and spatial distribution of tissue parts, and cells-to-tissue hierarchies. We assess the methodology on a large set of annotated tissue regions of interest from H&E stained breast carcinoma whole-slides.
  arXiv  Detail & Related papers  (2020-07-01T16:22:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.